# Remote Sensing Satellite Image-Based Monitoring of Agricultural Ecosystem

Authors: Ningjun Wang; Tiantian Liu; Xueying Tang; Qinglie Yuan
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In the agricultural ecosystem, it is necessary to grasp the differences of soil fertility and crop growth in time and space. With the rapid development of remote sensing and its popularization and application in social production practice, remote sensing has become a new way to obtain farmland information. At present, remote sensing means in the field of agroecosystem monitoring mainly include satellite remote sensing and unmanned aerial remote sensing. It can monitor and manage the agro-ecosystem environment in real time by acquiring remote sensing images. It can monitor the growth and identification of crops, pests, and diseases, and water supply, analysis timely, and effectivity for the spatial information provided by a feedback processing. But its performance needs to be improved. In this paper, the imaging methods and main features of remote sensing images are introduced in order to analyze the characteristics of agricultural ecosystem targets in remote sensing images. Then, based on object-oriented classification technology, a series of farmland images are preprocessed, segmented, and sparsely represented. Processing operations are used to study how to transform crop observation into crop and noncrop discrimination in remote sensing data. The research shows that the effective acquisition of crop image area and the remote sensing monitoring of crop farmland area can be achieved by processing the crop field map image obtained by remote sensing.


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## Body

## 1. Introduction

As a big agricultural country, China’s grain is very huge. The rise of global food is difficult to have a greater impact on my country’s food prices. The objective facts should confirm the relevant information of crop cultivation in time to provide scientific basis for national economic development and macrocontrol policy decision-making. Accurate and rapid acquisition of crop area and yield estimation information is of great significance for China to strengthen agricultural production management and formulate relevant food policies scientifically and rationally.Agricultural remote sensing [1, 2] is a comprehensive technology, which combines remote sensing technology with computer technology for agricultural production, agricultural development, and agricultural land survey. Since 1970s, the principle of spectroscopy [3] has made great progress in the field of remote sensing. By analyzing the spectral reflectance characteristics of various land cover and crops, the spatial distribution corresponding to the characteristic categories has been established. Remote sensing image objective function, color display, and other information of image interpretation markers have been widely used and successfully investigated agricultural resources, monitoring crops and estimating yield, crop disaster warning, and postdisaster assessment.At present, the methods of collecting crop-related information in China are mainly based on the following two methods. (1) The reports of administrative districts, counties, cities, and provinces are summarized to the State Statistical Bureau for data analysis and publication. (2) Samples from different regions are sampled by sampling and statistical methods to estimate the local crop information, and then, the statistical department collects the presidential statistics data for total crop information analysis. The statistical methods of these two methods are relatively long and inefficient. At the same time, they consume a lot of manpower, material, and financial resources. The final statistical data are often too large to obtain scientific results of statistical results and effective review. Compared with traditional statistical methods, remote sensing technology in agricultural applications has been developing continuously, saving time, materials, manpower, and talents, short cycle, wide range, high timeliness, and low cost. It significantly reduces the interference of economic and human values on statistical results and allows rapid and accurate extraction of crop planting area and yield estimates, thus producing very good economic and social values.High-resolution satellite remote sensing uses computer to process digital images directly, which makes it more difficult to classify crop targets [4, 5]. Satellite is far from the ground, and the image data collected is high-altitude imaging. Under the influence of satellite sensors, atmospheric environment, and illumination angle, various noises are generated in the image, which affects the display of target information in the image. In practical applications, people need to classify images accurately and process incomplete information. Remote sensing image data is very large, especially high-resolution image has more information and contains more target information than normal situation. More and more complex images may have blocking, spectral reflection, and other effects as well as interference between objects, which makes it difficult to extract objects of interest. Crops have their own development stages, different regions, different development stages, and different spectral information and imaging time, which greatly increases the difficulty of computer interpretation of remote sensing digital images.The remote sensing image expresses the difference of different features by the difference of luminance value or pixel value (reflecting the spectral information of the object) and spatial change (reflecting the spatial information of the object). Its purpose is to distinguish different objects in the image, which is a physical basis. Remote sensing images processed by computer must be digital images. The analog image obtained by photography must be converted by analog/digital (A/D) with an image scanner; the digital data obtained by scanning must be transferred to a general carrier such as CCT that can be read by a general digital computer. Computer image processing is carried out in an image processing system. The classification of remote sensing images is based on the analysis of the spectral, texture, and spatial characteristics of various objects in remote sensing images. According to specific rules or algorithms, each pixel of the image is divided into different categories to complete the classification of remote sensing images. Target information corresponding to real objects is obtained from remote sensing images. In the 1980s and 1990s, most classification techniques used image pixels as their basic units, and each pixel was represented by a single land use level coverage class. Pixel points are taken as the basic unit to study. A group of classification techniques has been developed, such as unsupervised [6, 7] (e.g., K-means and ISODATA), supervised [8, 9] (i.e., maximum likelihood, artificial neural network, support vector machine, random forest, and decision tree), and mixed classification (i.e., the fusion of semisupervised and supervised and unsupervised learning). The use of pixels as the basic unit of analysis has been developed. When applied to heterogeneous regions, the classification of these pixels is limited, and the size of the object may be much smaller than that of the pixel.In this paper, a new method for remote sensing image classification is proposed, which mainly uses object model. Compared with traditional pixel-by-pixel and subpixel classification methods, object-based models and geographic objects are recorded in basic analysis units and generated by image segmentation, rather than treating images as a set of individual pixels with spectral characteristics. Then, the image object is applied to the segmented object instead of the pixel. Finally, the spectral, spatial, texture, and background information of the segmented objects are analyzed, and the spectral and other related criteria for further classification of these objects are constructed. The irregular structure and region of clipping edge in image are analyzed. Fuzzy set theory is used to improve the gray contrast of edge regions, and rectangular farmland function is introduced into the graph-based segmentation theory to realize the effective farmland division in satellite remote sensing images. On the basis of sparse representation, crop/noncrop representation model is established between superpixels and image blocks generated by hypersegmentation. Crop extraction is classified into crop and noncrop classification in the image, marking of regional objects is completed, and crop extraction and final crop regional statistics are performed.


## 2. The Structure and Working Principle of Satellite Remote Sensing Detection System

Remote sensing is a way to understand the earth from various angles, including map mapping, agricultural and forestry resources protection, urban planning, marine resources detection, traffic flow control, weather flood control, river protection, livestock, and pasture control observation management, and data fusion mining applications are irreplaceable. Due to the existence of remote sensing target recognition technology in these specific applications, it can be seen that remote sensing target recognition has high commercial and military value.


### 2.1. Principle of Remote Sensing Imaging

Remote sensing refers to noncontact, long-distance detection technology and generally refers to the use of sensors/remote sensors to detect the radiation and reflection characteristics of electromagnetic waves of objects. Remote sensing is to detect target objects when far away from the target and noncontact target objects through instruments sensitive to electromagnetic waves such as remote sensors. It is a science and technology to obtain the reflected, radiated, or scattered electromagnetic wave information (such as electric field, magnetic field, electromagnetic wave, and seismic wave), and to extract, determine, process, analyze, and apply.Remote sensing of ground targets is due to the different spectral characteristics of the object itself, that is, the different absorption and reflection of the object when it is irradiated, and the radiation characteristics after energy absorption. When the sun shines on the earth’s surface, it first passes through the atmosphere, which has a specific absorption capacity for solar radiation. Because sunlight is a complex light source, different wavelengths of light have different permeability to the atmosphere, so after passing through it, the atmosphere is reflected and absorbed by other energy.The spectrum that transmits the strongest frequency band in the atmosphere is called the atmospheric window. The spectral bands of the atmospheric window are ultraviolet, visible, and near infrared. The results obtained in these bands are the main spectral bands for receiving remote sensing satellites at present. Therefore, when the sun shines on the surface of an object in the atmosphere, for the same spectral band, it will have different reflection results, and radiation from different spectral bands will be transmitted to the same object. Different data signals are also received after receiving the sensor. The remote sensing image obtained from the reflected spectrum signal obtained from this process (Figure1).Figure 1
Principle process diagram of satellite remote sensing imaging.


### 2.2. Main Categories of Optical Remote Sensing Imaging

Optical remote sensing technology is a technology for obtaining target and environmental information from a long distance in the ultraviolet to infrared optical band. Optical remote sensing systems are usually composed of remote sensors, remote sensing platforms, information transmission, and information processing equipment. Military is mainly used for reconnaissance, surveillance, missile warning, and weather forecasting [10–12].Figure2 shows three methods for classifying optical remote sensing images. Full color remote sensing image is a small wavelength image of 0.5 micron to 0.75 micron in visible spectrum. Because of the strong spectral penetration of this band, the resulting image is clear and generally used as a high-resolution image. However, the displayed image is a gray image, which cannot represent the color characteristics of the object.Figure 2
Classification of optical remote sensing imaging.Multispectral remote sensing image is a kind of remote sensing image, which combines spectral information of several bands. For example, remote sensing images are usually seen in red (R), green (G), and blue (B). The three-band spectral images are colored by R, G, and B, respectively, and then combined by a certain proportion of normalization. Finally, the color image which can represent the color features is obtained. Of course, spectral fusion of other optional frequency bands is used to obtain the required remote sensing images.In the visible band and near, middle, and far infrared band of infrared spectrum, spectral remote sensing images of spectral data are very narrow in tens or hundreds of wavelet bands. It can acquire the spatial characteristics of visible band in infrared spectrum and the radiation characteristics of objects, so that it can detect many features of objective and objective which are difficult to detect by remote sensing visible light and then detect some data which are difficult to detect on the surface of objects.


### 2.3. Structure of Satellite Remote Sensing System

Satellite remote sensing system for earth observation is represented by satellite platform system, satellite operation control system, data transmission processing system, and image data application system. The satellite platform system is mainly composed of space platform system and airborne sensors. The space platform system is equipped with a carrier to provide space flight for various types of observation equipment, responsible for processing and connection. Ground-to-ground transmission of image data received by sensors is the main satellite earth observation equipment. It observes and records ground objects as the eyes of the whole system and ground image system equipment based on sensors installed on the ground (such as visible light, infrared, and microwave).The satellite control system controls the satellite by integrating the satellite data of each station in the system. The satellite platform is equipped with detection equipment, attitude, and orbit operation through the ground command station, in order to correct the satellite’s operation route and control the satellite’s operation status and perform satellite operation and other operations.Satellite data transmission processing system includes ground receiving and transmission, communication data calibration, compression, conversion, data inversion, and other operations, access to data processing center for backup, data control, and other processes.The image data application system receives remote sensing image data from satellite observation image data and transmits the data to the ground processing system. Ground processing systems provide control for different industries, including positioning control, standard product production, advanced image processing, and typical industry applications and other products. Finally, a user object service using it is provided.Through the system being studied in our country, we are building a TMA system for remote sensing optical satellites, which covers a large number of space satellite ground networks and consists of many small optical satellites and satellites. In order to achieve the goal of reexamination in a short time, shoot the earth all day. At present, there are earth observation satellites, video satellites, and technology verification satellites. In the future, a large Earth observation network of 137 satellites and other orbiting satellites will be built to make new visits to Earth’s surface targets in 10 minutes.


### 2.4. Characteristics of Satellite Remote Sensing Images

In order to fully understand the processing of satellite remote sensing image in subsequent image processing, image description, and image recognition, we must first understand the various features of image features contained in the image. Remote sensing is convenient for research and processing. The image information is processed according to its characteristics.Figure3 shows that the main information content of remote sensing image includes three aspects: spatial feature, spectral feature, and temporal feature.Figure 3
Information content of remote sensing images.According to the acquisition method: aerial photos (aircraft shooting) and satellite photos (satellite shooting); according to spatial resolution: high-resolution imagery, medium-resolution imagery, and low-resolution imagery; according to spectral resolution: multispectral (high-resolution imagery) spectrum), panchromatic, visible light, far infrared, near infrared, etc.Spatial features refer to the content representation of remote sensing images in the spatial direction of image pixels, including spatial geometric contours, geometric regions, azimuth, coordinates, ranges, texture features, and other spatial data of image target information. The characteristics of synthetic space are mainly divided into two groups. One group is the pixel value of the pixel coordinates selected in the space, which must be digitized by the image. The other is spatial resolution, which is the number of pixels contained in the cell area with fixed size and fixed proportion. This is an important condition for measuring image information. In order to obtain the spatial information of a particular coordinate or location of an image, it is necessary for the spatial positioning system to locate the remote detection image in a fixed latitude and longitude coordinate by positioning the integrated sky and earth system, which is convenient for data analysis and modeling.Gray values (such as gray resolution or RGB values) represent the spectral characteristics of an image. In order to express the radiation intensity in the computer, the radiation intensity is divided into equal proportion radiation values by quantization method and stored in the computer according to the fixed coding method. The quantization range is represented by bits, and the quantization range corresponding ton bits is 0~2n−1.In fact, the spectral characteristics of objects in remote sensing images correspond to the absorption and reflection of solar radiation by objects, which also make remote sensing satellites reflect by receiving light reflection in different bands and then synthesize remote sensing images through different bands. According to the bits of quantized spectral intensity stored in the computer, it has become an important measure to measure the amount of information contained in the image, and it is also an important requirement of high quality remote sensing image.The temporal characteristics of remote sensing images refer to the changes of spectral and temporal characteristics of remote sensing images when they are accessed again at different times. Due to the movement of land transportation equipment, climate change, construction, seasonal replacement, plant growth and so on, remote sensing targets will undergo great spatial changes, which will lead to the spatial characteristics of images. Because the amount of radiation and the angle of illumination from the sun to the earth are different at different times of the day, the spectral characteristics of the image will change greatly, and then, it can be expressed in the image data through data processing.


## 2.1. Principle of Remote Sensing Imaging

Remote sensing refers to noncontact, long-distance detection technology and generally refers to the use of sensors/remote sensors to detect the radiation and reflection characteristics of electromagnetic waves of objects. Remote sensing is to detect target objects when far away from the target and noncontact target objects through instruments sensitive to electromagnetic waves such as remote sensors. It is a science and technology to obtain the reflected, radiated, or scattered electromagnetic wave information (such as electric field, magnetic field, electromagnetic wave, and seismic wave), and to extract, determine, process, analyze, and apply.Remote sensing of ground targets is due to the different spectral characteristics of the object itself, that is, the different absorption and reflection of the object when it is irradiated, and the radiation characteristics after energy absorption. When the sun shines on the earth’s surface, it first passes through the atmosphere, which has a specific absorption capacity for solar radiation. Because sunlight is a complex light source, different wavelengths of light have different permeability to the atmosphere, so after passing through it, the atmosphere is reflected and absorbed by other energy.The spectrum that transmits the strongest frequency band in the atmosphere is called the atmospheric window. The spectral bands of the atmospheric window are ultraviolet, visible, and near infrared. The results obtained in these bands are the main spectral bands for receiving remote sensing satellites at present. Therefore, when the sun shines on the surface of an object in the atmosphere, for the same spectral band, it will have different reflection results, and radiation from different spectral bands will be transmitted to the same object. Different data signals are also received after receiving the sensor. The remote sensing image obtained from the reflected spectrum signal obtained from this process (Figure1).Figure 1
Principle process diagram of satellite remote sensing imaging.


## 2.2. Main Categories of Optical Remote Sensing Imaging

Optical remote sensing technology is a technology for obtaining target and environmental information from a long distance in the ultraviolet to infrared optical band. Optical remote sensing systems are usually composed of remote sensors, remote sensing platforms, information transmission, and information processing equipment. Military is mainly used for reconnaissance, surveillance, missile warning, and weather forecasting [10–12].Figure2 shows three methods for classifying optical remote sensing images. Full color remote sensing image is a small wavelength image of 0.5 micron to 0.75 micron in visible spectrum. Because of the strong spectral penetration of this band, the resulting image is clear and generally used as a high-resolution image. However, the displayed image is a gray image, which cannot represent the color characteristics of the object.Figure 2
Classification of optical remote sensing imaging.Multispectral remote sensing image is a kind of remote sensing image, which combines spectral information of several bands. For example, remote sensing images are usually seen in red (R), green (G), and blue (B). The three-band spectral images are colored by R, G, and B, respectively, and then combined by a certain proportion of normalization. Finally, the color image which can represent the color features is obtained. Of course, spectral fusion of other optional frequency bands is used to obtain the required remote sensing images.In the visible band and near, middle, and far infrared band of infrared spectrum, spectral remote sensing images of spectral data are very narrow in tens or hundreds of wavelet bands. It can acquire the spatial characteristics of visible band in infrared spectrum and the radiation characteristics of objects, so that it can detect many features of objective and objective which are difficult to detect by remote sensing visible light and then detect some data which are difficult to detect on the surface of objects.


## 2.3. Structure of Satellite Remote Sensing System

Satellite remote sensing system for earth observation is represented by satellite platform system, satellite operation control system, data transmission processing system, and image data application system. The satellite platform system is mainly composed of space platform system and airborne sensors. The space platform system is equipped with a carrier to provide space flight for various types of observation equipment, responsible for processing and connection. Ground-to-ground transmission of image data received by sensors is the main satellite earth observation equipment. It observes and records ground objects as the eyes of the whole system and ground image system equipment based on sensors installed on the ground (such as visible light, infrared, and microwave).The satellite control system controls the satellite by integrating the satellite data of each station in the system. The satellite platform is equipped with detection equipment, attitude, and orbit operation through the ground command station, in order to correct the satellite’s operation route and control the satellite’s operation status and perform satellite operation and other operations.Satellite data transmission processing system includes ground receiving and transmission, communication data calibration, compression, conversion, data inversion, and other operations, access to data processing center for backup, data control, and other processes.The image data application system receives remote sensing image data from satellite observation image data and transmits the data to the ground processing system. Ground processing systems provide control for different industries, including positioning control, standard product production, advanced image processing, and typical industry applications and other products. Finally, a user object service using it is provided.Through the system being studied in our country, we are building a TMA system for remote sensing optical satellites, which covers a large number of space satellite ground networks and consists of many small optical satellites and satellites. In order to achieve the goal of reexamination in a short time, shoot the earth all day. At present, there are earth observation satellites, video satellites, and technology verification satellites. In the future, a large Earth observation network of 137 satellites and other orbiting satellites will be built to make new visits to Earth’s surface targets in 10 minutes.


## 2.4. Characteristics of Satellite Remote Sensing Images

In order to fully understand the processing of satellite remote sensing image in subsequent image processing, image description, and image recognition, we must first understand the various features of image features contained in the image. Remote sensing is convenient for research and processing. The image information is processed according to its characteristics.Figure3 shows that the main information content of remote sensing image includes three aspects: spatial feature, spectral feature, and temporal feature.Figure 3
Information content of remote sensing images.According to the acquisition method: aerial photos (aircraft shooting) and satellite photos (satellite shooting); according to spatial resolution: high-resolution imagery, medium-resolution imagery, and low-resolution imagery; according to spectral resolution: multispectral (high-resolution imagery) spectrum), panchromatic, visible light, far infrared, near infrared, etc.Spatial features refer to the content representation of remote sensing images in the spatial direction of image pixels, including spatial geometric contours, geometric regions, azimuth, coordinates, ranges, texture features, and other spatial data of image target information. The characteristics of synthetic space are mainly divided into two groups. One group is the pixel value of the pixel coordinates selected in the space, which must be digitized by the image. The other is spatial resolution, which is the number of pixels contained in the cell area with fixed size and fixed proportion. This is an important condition for measuring image information. In order to obtain the spatial information of a particular coordinate or location of an image, it is necessary for the spatial positioning system to locate the remote detection image in a fixed latitude and longitude coordinate by positioning the integrated sky and earth system, which is convenient for data analysis and modeling.Gray values (such as gray resolution or RGB values) represent the spectral characteristics of an image. In order to express the radiation intensity in the computer, the radiation intensity is divided into equal proportion radiation values by quantization method and stored in the computer according to the fixed coding method. The quantization range is represented by bits, and the quantization range corresponding ton bits is 0~2n−1.In fact, the spectral characteristics of objects in remote sensing images correspond to the absorption and reflection of solar radiation by objects, which also make remote sensing satellites reflect by receiving light reflection in different bands and then synthesize remote sensing images through different bands. According to the bits of quantized spectral intensity stored in the computer, it has become an important measure to measure the amount of information contained in the image, and it is also an important requirement of high quality remote sensing image.The temporal characteristics of remote sensing images refer to the changes of spectral and temporal characteristics of remote sensing images when they are accessed again at different times. Due to the movement of land transportation equipment, climate change, construction, seasonal replacement, plant growth and so on, remote sensing targets will undergo great spatial changes, which will lead to the spatial characteristics of images. Because the amount of radiation and the angle of illumination from the sun to the earth are different at different times of the day, the spectral characteristics of the image will change greatly, and then, it can be expressed in the image data through data processing.


## 3. Main Methods of Satellite Remote Sensing Image Preprocessing

At present, the object of remote sensing image is a method of object recognition based on the whole space-space-earth system, and the recognition of space satellite system with ground station is the goal of cultivation in agricultural ecosystem. Through continuous scanning of crop targets, remote scanning satellites with large scanning by remote sensing system monitoring network can search the targets of vast farmland and then analyze the crops. After that, the target area is marked and located on the image, and then, the target range is sent to the high-resolution remote sensing satellite to locate the target image accurately. The operating system of the high-resolution ground observation satellite and the ground station is coordinated. The target is locked by the target recognition method and the information needed is finally extracted.The main framework of traditional objective image recognition is divided into the following four steps (Figure4): target image preprocessing, remote sensing of original image for denoising, edge sharpening, wavelet transform, cloud removal, and other processing, and then further processing the target; and image segmentation, using image segmentation technology to extract target sample image from background image for processing. For the following steps of sample template, feature extraction is extracting the value of the target feature of the segmented image to be processed for subsequent recognition processing; target recognition, through matching or classifier training algorithm, classifies and calibrates the extracted target and eliminates false warning and other data acquisition work.Figure 4
Processing flow of remote sensing image.


### 3.1. Image Enhancement Based on Laplacian Operator

Satellite-acquired remote sensing images of initial visible light are processed in multilevel correction, filtering, matching, and so on to display large-scale original remote sensing images completely and clearly. The remote sensing target in farmland cannot be directly used for target recognition because of the interference of crop target in farmland. Therefore, it is necessary to highlight the interesting crop targets through the processing as shown in the figure.Because the color of crop target is very similar to that of weeds, in order to facilitate the subsequent recognition, it is necessary to enhance the enhanced remote sensing crop target in the farmland, improve the recognition degree of the target, and improve the recognition effect. Generally, filtering method is used to enhance the whole image.Firstly, the digital image is defined as a three-dimensional functionfx,y,z, where x,y is a two-dimensional spatial plane function, which generally represents a pixel, and z is the dimension of the pixels at x,y points. The definition of Laplacian operator is
(1)∇2f=∂2f∂x2+∂2f∂y2.This Laplace operator is a linear operator. The second-order differential operator is transformed into difference form based onx direction.
(2)∂2f∂x2=fx+1,y+fx−1,y−2fx,y.Convert second-order differential operators to differential forms based ony direction.
(3)∂2f∂y2=fx,y+1+fx,y−1−2fx,y.By substituting equality (2) and equality (3) into equality (1), discretized Laplace operators are obtained.
(4)∇2f=fx,y+1+fx,y−1+fx−1,y−4fx,y.Laplacian filtering of remote sensing images using(5)gx,y=fx,y+c∇2fx,y.The crop remote sensing image is enhanced by the operation of equation (5).


### 3.2. Remote Sensing Image Denoising and Filtering

Because the sensor received by remote sensing satellite receives the signal, the transmitted image is often polluted by impulse noise, which affects the output image. The median filter is used for noise filtering to eliminate impulse noise. The median filter formula is(6)gx,y=medfx−k,y−1,k,l∈W.In Equation (6), Med denotes an intermediate operation to read from the entire convolution kernel using a median operation, where W is the size of the convolution kernel. The median filtering algorithm is to filter the model W in order to filter the similarly sized regions of W. Some algorithms usually use pairs by sorting the pixel values in W and then selecting the median to output the results. The convolution kernels are first sorted by bubbles and then compared. Each time it moves, W will sort internally until the end of the filter.


### 3.3. Research on Remote Sensing Image Segmentation Method

In image processing methods, image segmentation plays an important role in image processing methods. It is also a difficult part of image processing methods. It is different from the previous image processing methods and the spatial region of the previous image. The spatial domain, frequency, and conversion processing of remote sensing image are the whole image problem, and image segmentation is used to extract part of the region from the whole image for subsequent processing, mainly according to the relevant attributes of the image to perform the relevant region segmentation processing.Because of the complexity of remote sensing targets in segmentation, it is necessary to use CV model to solve the problem of good edge detection ability for target contour. Moreover, previous geometric contour segmentation is not allowed to effectively segment the target in the whole region, and more is to segment complex scattered targets or targets with more hollow points. In addition, by performing image segmentation on color images, the visible remote sensing image data set can be generated after subsequent image segmentation. In a word, an active contour model based on region segmentation based on previous geometric active contour model is proposed. The improved active contour model based on maximum segmentation theory and evolution of Gauss model is the method of active contour segmentation for the whole image.Curve evolution can be effectively achieved by narrowband level set. Assuming that the image size isM∗N, approximately M2N2/2 data will be saved in the calculation process. This is equivalent to a symmetric matrix W of MN∗MN size, in which the j column element of row I iswpi,pj. Even for small images, this kind of calculation will take up a considerable amount of memory in the CPU, which is difficult to calculate and expand. In this section, a more effective method for calculating the dissimilarity is proposed.A matrixW with the size of MN∗mnis designed for the designed structural plane, in which m<<M, n<<NW. Each element in W′ corresponds to the difference of each pixel (x,y) in the subregion. In order to segment the image area, the image is divided into m∗n equal blocks, and Fx,y represents the average value of each image feature.
(7)Fij′=1Aij∫TijFx,ydxdy.The coordinatesx,y of each element in W′ are converted to regional Tij through Fx,y−Fij′. For every point C on the curve, its sum is the sum of all regions inside and outside the curve. Let Tkk=1,2,⋯,mn be the divided area and Pk be the center coordinate of Tk. The moment center is used to represent the characteristics of each region. This method will also measure the difference between the work and the spatial expression. For different implementations, median or integral estimation can also be used.Image segmentation algorithm cannot use the change of pixel intensity as the basis of segmentation nor can it use a single segmentation threshold to determine the segmentation. Each node in the image represents the node in the graph, and the undirected edge of each node has a weight to measure the dissimilarity between the two nodes. According to the change rate of adjacent regions on the feature threshold, the segmentation threshold is adjusted dynamically, and the edge extraction image is segmented to obtain the final extraction result.


## 3.1. Image Enhancement Based on Laplacian Operator

Satellite-acquired remote sensing images of initial visible light are processed in multilevel correction, filtering, matching, and so on to display large-scale original remote sensing images completely and clearly. The remote sensing target in farmland cannot be directly used for target recognition because of the interference of crop target in farmland. Therefore, it is necessary to highlight the interesting crop targets through the processing as shown in the figure.Because the color of crop target is very similar to that of weeds, in order to facilitate the subsequent recognition, it is necessary to enhance the enhanced remote sensing crop target in the farmland, improve the recognition degree of the target, and improve the recognition effect. Generally, filtering method is used to enhance the whole image.Firstly, the digital image is defined as a three-dimensional functionfx,y,z, where x,y is a two-dimensional spatial plane function, which generally represents a pixel, and z is the dimension of the pixels at x,y points. The definition of Laplacian operator is
(1)∇2f=∂2f∂x2+∂2f∂y2.This Laplace operator is a linear operator. The second-order differential operator is transformed into difference form based onx direction.
(2)∂2f∂x2=fx+1,y+fx−1,y−2fx,y.Convert second-order differential operators to differential forms based ony direction.
(3)∂2f∂y2=fx,y+1+fx,y−1−2fx,y.By substituting equality (2) and equality (3) into equality (1), discretized Laplace operators are obtained.
(4)∇2f=fx,y+1+fx,y−1+fx−1,y−4fx,y.Laplacian filtering of remote sensing images using(5)gx,y=fx,y+c∇2fx,y.The crop remote sensing image is enhanced by the operation of equation (5).


## 3.2. Remote Sensing Image Denoising and Filtering

Because the sensor received by remote sensing satellite receives the signal, the transmitted image is often polluted by impulse noise, which affects the output image. The median filter is used for noise filtering to eliminate impulse noise. The median filter formula is(6)gx,y=medfx−k,y−1,k,l∈W.In Equation (6), Med denotes an intermediate operation to read from the entire convolution kernel using a median operation, where W is the size of the convolution kernel. The median filtering algorithm is to filter the model W in order to filter the similarly sized regions of W. Some algorithms usually use pairs by sorting the pixel values in W and then selecting the median to output the results. The convolution kernels are first sorted by bubbles and then compared. Each time it moves, W will sort internally until the end of the filter.


## 3.3. Research on Remote Sensing Image Segmentation Method

In image processing methods, image segmentation plays an important role in image processing methods. It is also a difficult part of image processing methods. It is different from the previous image processing methods and the spatial region of the previous image. The spatial domain, frequency, and conversion processing of remote sensing image are the whole image problem, and image segmentation is used to extract part of the region from the whole image for subsequent processing, mainly according to the relevant attributes of the image to perform the relevant region segmentation processing.Because of the complexity of remote sensing targets in segmentation, it is necessary to use CV model to solve the problem of good edge detection ability for target contour. Moreover, previous geometric contour segmentation is not allowed to effectively segment the target in the whole region, and more is to segment complex scattered targets or targets with more hollow points. In addition, by performing image segmentation on color images, the visible remote sensing image data set can be generated after subsequent image segmentation. In a word, an active contour model based on region segmentation based on previous geometric active contour model is proposed. The improved active contour model based on maximum segmentation theory and evolution of Gauss model is the method of active contour segmentation for the whole image.Curve evolution can be effectively achieved by narrowband level set. Assuming that the image size isM∗N, approximately M2N2/2 data will be saved in the calculation process. This is equivalent to a symmetric matrix W of MN∗MN size, in which the j column element of row I iswpi,pj. Even for small images, this kind of calculation will take up a considerable amount of memory in the CPU, which is difficult to calculate and expand. In this section, a more effective method for calculating the dissimilarity is proposed.A matrixW with the size of MN∗mnis designed for the designed structural plane, in which m<<M, n<<NW. Each element in W′ corresponds to the difference of each pixel (x,y) in the subregion. In order to segment the image area, the image is divided into m∗n equal blocks, and Fx,y represents the average value of each image feature.
(7)Fij′=1Aij∫TijFx,ydxdy.The coordinatesx,y of each element in W′ are converted to regional Tij through Fx,y−Fij′. For every point C on the curve, its sum is the sum of all regions inside and outside the curve. Let Tkk=1,2,⋯,mn be the divided area and Pk be the center coordinate of Tk. The moment center is used to represent the characteristics of each region. This method will also measure the difference between the work and the spatial expression. For different implementations, median or integral estimation can also be used.Image segmentation algorithm cannot use the change of pixel intensity as the basis of segmentation nor can it use a single segmentation threshold to determine the segmentation. Each node in the image represents the node in the graph, and the undirected edge of each node has a weight to measure the dissimilarity between the two nodes. According to the change rate of adjacent regions on the feature threshold, the segmentation threshold is adjusted dynamically, and the edge extraction image is segmented to obtain the final extraction result.


## 4. Design of Agricultural Monitoring Based on Remote Sensing Image

In this chapter, crop extraction is classified as crop and noncrop classification in images, and local objects are shown through classification methods. Using the segmentation method proposed above, the studied image is segmented into better region objects with better internal consistency. In this chapter, we will extract the target features of crop clipping and the original images of other backgrounds (such as buildings and roads). At the same time, we will use the superpixels obtained by oversegmentation to model crops and noncrops. We will use the category attributes in sparse representation to discriminate the image blocks. Finally, in order to obtain the final crop, the results were extracted, and multifunctional decision was made to complete the statistical work of crop field area.


### 4.1. Feature Extraction

To further classify the segmented image block markers, it is necessary to characterize the target objects, such as spectrum, texture, and some derived features. In order to reduce the complexity of image classification and reduce the relationship between features and the amount of information to be concerned, the differences between other features of crops and noncrops are analyzed in this paper, so as to reduce the complexity of image classification. According to the statistical characteristics of the original data, the statistical features of all the image targets are more complete with as few features as possible. In this paper, three features are selected as effective features for crop extraction.


#### 4.1.1. Spectral Characteristics

The spectral characteristics of crops are the main basis for crop farmland classification in remote sensing images. They are the measurements of electromagnetic radiation of crop targets in different bands. These measurements can be used as the original feature variables of remote sensing image classification. In this paper, the gray valueρr, ρe, ρg, and PNR of Band1 (red), Band2 (green), Band3 (blue), and Band4 (near infrared) bands in satellite data can be used as direct spectral characteristics of describing ground objects to participate in classification.


#### 4.1.2. Texture Features

Gray-scale image texture features can provide context, structure, and spatial information of objects and reflect irregular attributes within image blocks. They are not only dependent on surface features but also related to illumination angle and image contrast. They are variable values and are widely used in remote sensing image classification. In this paper, we extract color histogram, edge direction histogram (which is more robust to illumination changes), and some texture feature statistics described by gray level cooccurrence matrixPi,j.


#### 4.1.3. Vegetation Index

Band1, Band2, and Band3 are visible bands in the GF-1 image, and plant pigment cells have a great influence on the shape of the target image. Another near infrared Band4 reflects vegetation information such as plant growth and leaf index. As typical vegetation, crops can be extracted from crops by obtaining the information index that responds to vegetation.


### 4.2. Establishing Sparse Representation Model for Recognition Target

The main idea of this chapter is to establish a crop/noncrop sparse representation model between image blocks obtained by segmentation method and superpixels generated by oversegmentation and to set up a superpixel dictionary to create image blocks from segmentation results. The final results of crop extraction were obtained by sorting. Firstly, the segmentation algorithm is used to divide the image, and thousands of object regions are recorded as image blocks. In computer image processing, crops and noncrops can be approximately composed of multiple image blocks. Secondly, watershed method is used to divide the image into several smaller regions.This paper introduces the concept of “superpixel,” which subdivides an image into subregions of multiple pixel sets, which are small regions composed of a series of pixels with similar positions, such as color, brightness, and texture. Most of these small areas retain effective information for further image segmentation and usually do not destroy the object boundary information in the image. Because this paper is consistent and includes the local features of the image, it stores as much structural information as possible to select the superpixels for processing and divides them into different scales. Each superpixel in the result of oversegmentation can be divided into about one image block. Therefore, in this paper, we consider that each segmented image block consists of several superpixels. Based on the segmentation results of image blocks, crop field extraction from remote sensing images is transformed into crop/noncrop image block classification. The feature extraction of subsequent image blocks is based on the superpixel generation of each image block. Find the coefficient vectors to represent and then use the coefficient vectors to perform the target classification.


### 4.3. Establishing Sparse Representation of Crop/Noncrop Model

Based on the above concepts, the sample area of artificial interpretation can be approximated by thousands of superpixels belonging to crop or noncrop species, and the common matrixM of both crop and noncrop samples can be modeled simultaneously.Firstly, combined with the field detection data managed by GPS, using manual interpretation and classification results as the benchmark for extracting the regions belonging to crop categories, the regions belonging to crop categories are extracted, and the rest are noncrop. Then, the image is segmented by watershed segmentation algorithm to obtain superpixel images, many of which belong to crop areas.Based on column vectors representing NDVI eigenvalues of superpixels of crops and noncrops, matrixM is generated by crop/noncrop model, which can be expressed as:
(8)M=f1,f2,⋯,fp,f1,f2,⋯,fq.The segmented image obtained by this method is defined as a series of image blocks. Image blocks are composed of several superpixels, so this chapter analyses their sparsity through mining. The proposed segmentation method is used to divide the classified image into several image blocks. Each image block is recorded asα, and its eigenvalue Fα is calculated. Each image block is linearly represented by a training model as follows:
(9)Fα=M×yα.The coefficient vectoryα is obtained by solving n linear equations. Only when the image block contains the superpixels of the training model, can an element in the coefficient vector of the object be nonzero. This completes the reconstruction of image blocks. At the same time, the current image block is classified according to the superpixel which determines the category in the training model.


## 4.1. Feature Extraction

To further classify the segmented image block markers, it is necessary to characterize the target objects, such as spectrum, texture, and some derived features. In order to reduce the complexity of image classification and reduce the relationship between features and the amount of information to be concerned, the differences between other features of crops and noncrops are analyzed in this paper, so as to reduce the complexity of image classification. According to the statistical characteristics of the original data, the statistical features of all the image targets are more complete with as few features as possible. In this paper, three features are selected as effective features for crop extraction.


### 4.1.1. Spectral Characteristics

The spectral characteristics of crops are the main basis for crop farmland classification in remote sensing images. They are the measurements of electromagnetic radiation of crop targets in different bands. These measurements can be used as the original feature variables of remote sensing image classification. In this paper, the gray valueρr, ρe, ρg, and PNR of Band1 (red), Band2 (green), Band3 (blue), and Band4 (near infrared) bands in satellite data can be used as direct spectral characteristics of describing ground objects to participate in classification.


### 4.1.2. Texture Features

Gray-scale image texture features can provide context, structure, and spatial information of objects and reflect irregular attributes within image blocks. They are not only dependent on surface features but also related to illumination angle and image contrast. They are variable values and are widely used in remote sensing image classification. In this paper, we extract color histogram, edge direction histogram (which is more robust to illumination changes), and some texture feature statistics described by gray level cooccurrence matrixPi,j.


### 4.1.3. Vegetation Index

Band1, Band2, and Band3 are visible bands in the GF-1 image, and plant pigment cells have a great influence on the shape of the target image. Another near infrared Band4 reflects vegetation information such as plant growth and leaf index. As typical vegetation, crops can be extracted from crops by obtaining the information index that responds to vegetation.


## 4.1.1. Spectral Characteristics

The spectral characteristics of crops are the main basis for crop farmland classification in remote sensing images. They are the measurements of electromagnetic radiation of crop targets in different bands. These measurements can be used as the original feature variables of remote sensing image classification. In this paper, the gray valueρr, ρe, ρg, and PNR of Band1 (red), Band2 (green), Band3 (blue), and Band4 (near infrared) bands in satellite data can be used as direct spectral characteristics of describing ground objects to participate in classification.


## 4.1.2. Texture Features

Gray-scale image texture features can provide context, structure, and spatial information of objects and reflect irregular attributes within image blocks. They are not only dependent on surface features but also related to illumination angle and image contrast. They are variable values and are widely used in remote sensing image classification. In this paper, we extract color histogram, edge direction histogram (which is more robust to illumination changes), and some texture feature statistics described by gray level cooccurrence matrixPi,j.


## 4.1.3. Vegetation Index

Band1, Band2, and Band3 are visible bands in the GF-1 image, and plant pigment cells have a great influence on the shape of the target image. Another near infrared Band4 reflects vegetation information such as plant growth and leaf index. As typical vegetation, crops can be extracted from crops by obtaining the information index that responds to vegetation.


## 4.2. Establishing Sparse Representation Model for Recognition Target

The main idea of this chapter is to establish a crop/noncrop sparse representation model between image blocks obtained by segmentation method and superpixels generated by oversegmentation and to set up a superpixel dictionary to create image blocks from segmentation results. The final results of crop extraction were obtained by sorting. Firstly, the segmentation algorithm is used to divide the image, and thousands of object regions are recorded as image blocks. In computer image processing, crops and noncrops can be approximately composed of multiple image blocks. Secondly, watershed method is used to divide the image into several smaller regions.This paper introduces the concept of “superpixel,” which subdivides an image into subregions of multiple pixel sets, which are small regions composed of a series of pixels with similar positions, such as color, brightness, and texture. Most of these small areas retain effective information for further image segmentation and usually do not destroy the object boundary information in the image. Because this paper is consistent and includes the local features of the image, it stores as much structural information as possible to select the superpixels for processing and divides them into different scales. Each superpixel in the result of oversegmentation can be divided into about one image block. Therefore, in this paper, we consider that each segmented image block consists of several superpixels. Based on the segmentation results of image blocks, crop field extraction from remote sensing images is transformed into crop/noncrop image block classification. The feature extraction of subsequent image blocks is based on the superpixel generation of each image block. Find the coefficient vectors to represent and then use the coefficient vectors to perform the target classification.


## 4.3. Establishing Sparse Representation of Crop/Noncrop Model

Based on the above concepts, the sample area of artificial interpretation can be approximated by thousands of superpixels belonging to crop or noncrop species, and the common matrixM of both crop and noncrop samples can be modeled simultaneously.Firstly, combined with the field detection data managed by GPS, using manual interpretation and classification results as the benchmark for extracting the regions belonging to crop categories, the regions belonging to crop categories are extracted, and the rest are noncrop. Then, the image is segmented by watershed segmentation algorithm to obtain superpixel images, many of which belong to crop areas.Based on column vectors representing NDVI eigenvalues of superpixels of crops and noncrops, matrixM is generated by crop/noncrop model, which can be expressed as:
(8)M=f1,f2,⋯,fp,f1,f2,⋯,fq.The segmented image obtained by this method is defined as a series of image blocks. Image blocks are composed of several superpixels, so this chapter analyses their sparsity through mining. The proposed segmentation method is used to divide the classified image into several image blocks. Each image block is recorded asα, and its eigenvalue Fα is calculated. Each image block is linearly represented by a training model as follows:
(9)Fα=M×yα.The coefficient vectoryα is obtained by solving n linear equations. Only when the image block contains the superpixels of the training model, can an element in the coefficient vector of the object be nonzero. This completes the reconstruction of image blocks. At the same time, the current image block is classified according to the superpixel which determines the category in the training model.


## 5. Evaluation Criterion

In this paper, we evaluate the subjective and objective classification results to verify the effectiveness of the classification method.


### 5.1. Subjective Assessment

Subjective assessment is a method of assessing images obtained mainly by manual methods. Although there are different evaluation criteria mainly to feel the visual perception of the first eye to the human eye, people’s evaluation of the actual classification quality is similar. Subjective evaluation is mainly used in some informal control places, but the actual use is less.


### 5.2. Objective Evaluation

This method is based on objective data. Because of the difference of thresholds, the spectral difference and noncrop characteristics of crops may be related to crops. Similarly, some pixels may be misclassified, and the target may be divided into several parts. In order to evaluate the quality of classification results more accurately and to evaluate segmentation methods, the experiment uses supervised objects based on pixel statistics. The evaluation method uses artificially interpreted images as a benchmark to analyze crop classification results and defines the following contents:(1)
Misclassification rate: it indicates that some noncrop pixels are misclassified into the ratio of crop human to artificial interpretation sample in the classification results(10)ER=Iseg−Ithruth∩IsegIthruth.(2)
Leakage rate: it indicates that some crop pixels in the classification result are misclassified into the ratio of noncrop human to artificial interpretation sample(11)NR=Ithruth−Ithruth∩IsegIthruth.(3)
Similarity: a similarity between the classification results and the real results is obtained by comparing the combination of the real crop classes and the artificial interpretation samples with the classification results(12)SD=Ithruth∩IsegIthruth∪Iseg.The accuracy of crop area was obtained by comparing the area ratio of real crop human and manual interpretation sample in statistical classification results.(13)AC=Ithruth∩IsegIthruth.


## 5.1. Subjective Assessment

Subjective assessment is a method of assessing images obtained mainly by manual methods. Although there are different evaluation criteria mainly to feel the visual perception of the first eye to the human eye, people’s evaluation of the actual classification quality is similar. Subjective evaluation is mainly used in some informal control places, but the actual use is less.


## 5.2. Objective Evaluation

This method is based on objective data. Because of the difference of thresholds, the spectral difference and noncrop characteristics of crops may be related to crops. Similarly, some pixels may be misclassified, and the target may be divided into several parts. In order to evaluate the quality of classification results more accurately and to evaluate segmentation methods, the experiment uses supervised objects based on pixel statistics. The evaluation method uses artificially interpreted images as a benchmark to analyze crop classification results and defines the following contents:(1)
Misclassification rate: it indicates that some noncrop pixels are misclassified into the ratio of crop human to artificial interpretation sample in the classification results(10)ER=Iseg−Ithruth∩IsegIthruth.(2)
Leakage rate: it indicates that some crop pixels in the classification result are misclassified into the ratio of noncrop human to artificial interpretation sample(11)NR=Ithruth−Ithruth∩IsegIthruth.(3)
Similarity: a similarity between the classification results and the real results is obtained by comparing the combination of the real crop classes and the artificial interpretation samples with the classification results(12)SD=Ithruth∩IsegIthruth∪Iseg.The accuracy of crop area was obtained by comparing the area ratio of real crop human and manual interpretation sample in statistical classification results.(13)AC=Ithruth∩IsegIthruth.


## 6. Experiments

Before processing the acquired satellite remote sensing images, image preprocessing algorithm and image pixel denoising are performed. This can further accurately obtain the monitoring of agricultural ecosystem, and is conducive to the acquisition of target eigenvalues, as well as the establishment of crop/noncrop models using sparse matrix representation. The farmland image material used in this paper is from the Internet (http://tushuo.jk51.com/tushuo/4097702_p4.html), as shown in Figure 5.Figure 5
Original image.Figure6 shows the gray image of the original image, which is also used to measure the color resolution of the image. Image depth determines the number of possible colors per pixel of a color image or the number of possible gray levels per pixel of a gray image. It determines the maximum number of colors in a color image or the maximum gray level in a gray image. Gray image is the basis of image processing.Figure 6
Gray image.


### 6.1. Image Enhancement and Filtering

In order to clearly verify the superiority of the algorithm in this paper, we first add white Gaussian noise to the gray-scale image to realize the simulation interference and then get the noise point image of Figure7. As we all know, there are many noises in the original image. It can be clearly seen that there are many white noises in the image after adding noises. Although these noises seem to have less impact on the global image, in fact, the global pixels have an impact on the segmentation threshold when image segmentation is carried out. Figure 8 is the final filtered image. From the image, we can see that the image quality has been improved obviously. And the image skip is not very big after filtering. It is a spatial filtering technology with low frequency enhancement. Generally, it is calculated by simple average method, that is, to find the average brightness value of adjacent pixels. After filtering, the white pixels disappear, which makes the image look smoother, and the snowflakes like noise are completely eliminated.Figure 7
Noise image.Figure 8
Filtered image.Figure9 shows the binary image after filtering. Compared with Figure 8, the binary image can clearly display the main information in the image by changing the image into 0 and 1 color images through a certain threshold.Figure 9
Binary image.


### 6.2. Image Segmentation

Figures10 and 11 show the results of image segmentation by different algorithms. Both results can roughly recognize the shape of farmland. However, the edge detection and segmentation image in Figure 10 is still imperfect. Two areas are segmented on the way. But in Figure 11, almost all farmland is segmented, and the texture features of farmland can be clearly seen. The planting direction and shape of different farmland can be clearly seen. The shape of different farmland can be clearly identified. At the same time, the segmentation method in this paper can increase the recognition rate of farmland.Figure 10
Edge detection and segmentation.Figure 11
The segmentation method in this paper.Figure12 shows the different threshold points of the two segmentation methods. When their independent variables and dependent variables are the same, it is obvious that the segmentation algorithm in this paper is better than the edge segmentation algorithm. From the graph, we can see that the segmentation algorithm in this paper can increase the smoothness of the pixel value and can get the segmentation threshold more accurately, so as to increase the accuracy of segmentation. The pixel value of edge segmentation algorithm is not smooth enough, which leads to incomplete segmentation after segmentation.Figure 12
Threshold comparison of two segmentation methods ((a) for this article, (b) for edge segmentation).


### 6.3. Comparison of Farmland Segmentation and Recognition Results

Figure13 shows the result of image recognition after segmentation. The blue and black parts of the result represent the recognized farmland. The difference of color is the recognition of different kinds and different crop colors. Generally speaking, the classification results of this method are in line with the actual farmland distribution and only in the right mountainous area with complex vegetation coverage has a greater impact on the classification results. According to statistics, ER, NR, and SD are 15.6%, 12.4%, and 76.5%, respectively. The area statistics of crops classified in this paper are as follows: AS=14320.7m2. Artificial interpretation of the crop area is 17595.4 m2, calculated the accuracy of farmland area AC=81.3%. Statistical work was carried out on crop regions of remote sensing images to meet the pilot application requirements of agricultural remote sensing center.Figure 13
Farmland recognition results from segmented images.


### 6.4. Contrast of Image Recognition Indicators

As can be seen from Table1, the classification results obtained by multifeature decision-making in this paper are superior to those obtained by single feature in both error rate and similarity, and the leak rate also shows better performance. The ER value of ρB, the NR value of ρG, and the SD value of ρNIR are the highest. Overall, it is better than a single feature. The real crop area in the classification result is obtained by multiplying the number of pixels of the real crop and the spatial resolution of the image. Spatial resolution refers to the minimum distance between two adjacent objects that can be recognized in remote sensing images. The accuracy of the classification method in this paper is AC=81.3%, which basically identifies the area of farmland accurately. As an approximate solution, it can almost fully perform its functions. As can be seen in Figure 14, the leakage rate of the three gamuts is relatively low, which proves that the segmentation can be basically accurate in the three gamuts.Table 1
Classification evaluation table for different gamut.
ER (%)NR (%)SD (%)ρR37.915.362.5ρG56.310.661.7ρB80.17.352.6ρNIR33.68.368.4Figure 14
Comparison of different segmentation rates of three gamuts.In order to prove the validity of this method in extracting agricultural targets from GF-1 satellite remote sensing images. Under the combination of the same feature spaceρR, ρG, ρB, ρNIR, CON, ENT, IDM, NDVI, DVI, and PVI, three methods of classification regression tree (CART), SVM (support vector machine) and random forest are used to classify the same region. The three indicators of statistical error rate, missing rate, and similarity are shown in Table 1.Table2 shows that the classification method in this paper is usually better than other methods, with the highest difference and similarity, and the lowest leakage rate, because the function selection is accurate. Because of the influence of target extraction, the features selected in this paper are not enough to fully reflect the target information, and the interaction between features is not considered. Classification regression tree needs manual feature threshold setting, but threshold setting directly affects classification accuracy, but threshold setting based on manual experience usually does not achieve the best classification effect, and is not suitable for other scenarios. As a commonly used classification algorithm in engineering, SVM is also inferior to this method in dealing with agriculture with high spectral complexity. Random forest algorithm has fewer parameters and does not need to standardize its functions. The classification results are close to this method. But the overall indicators are still not as good as the methods in this paper.Table 2
Comparison of classification algorithms.
ER (%)NR (%)SD (%)Classification and regression tree (CART)30.28.067.5SVM (support vector machine)20.413.570.6Random forest12.59.780.1Multifeature classification method4.25.695.8


## 6.1. Image Enhancement and Filtering

In order to clearly verify the superiority of the algorithm in this paper, we first add white Gaussian noise to the gray-scale image to realize the simulation interference and then get the noise point image of Figure7. As we all know, there are many noises in the original image. It can be clearly seen that there are many white noises in the image after adding noises. Although these noises seem to have less impact on the global image, in fact, the global pixels have an impact on the segmentation threshold when image segmentation is carried out. Figure 8 is the final filtered image. From the image, we can see that the image quality has been improved obviously. And the image skip is not very big after filtering. It is a spatial filtering technology with low frequency enhancement. Generally, it is calculated by simple average method, that is, to find the average brightness value of adjacent pixels. After filtering, the white pixels disappear, which makes the image look smoother, and the snowflakes like noise are completely eliminated.Figure 7
Noise image.Figure 8
Filtered image.Figure9 shows the binary image after filtering. Compared with Figure 8, the binary image can clearly display the main information in the image by changing the image into 0 and 1 color images through a certain threshold.Figure 9
Binary image.


## 6.2. Image Segmentation

Figures10 and 11 show the results of image segmentation by different algorithms. Both results can roughly recognize the shape of farmland. However, the edge detection and segmentation image in Figure 10 is still imperfect. Two areas are segmented on the way. But in Figure 11, almost all farmland is segmented, and the texture features of farmland can be clearly seen. The planting direction and shape of different farmland can be clearly seen. The shape of different farmland can be clearly identified. At the same time, the segmentation method in this paper can increase the recognition rate of farmland.Figure 10
Edge detection and segmentation.Figure 11
The segmentation method in this paper.Figure12 shows the different threshold points of the two segmentation methods. When their independent variables and dependent variables are the same, it is obvious that the segmentation algorithm in this paper is better than the edge segmentation algorithm. From the graph, we can see that the segmentation algorithm in this paper can increase the smoothness of the pixel value and can get the segmentation threshold more accurately, so as to increase the accuracy of segmentation. The pixel value of edge segmentation algorithm is not smooth enough, which leads to incomplete segmentation after segmentation.Figure 12
Threshold comparison of two segmentation methods ((a) for this article, (b) for edge segmentation).


## 6.3. Comparison of Farmland Segmentation and Recognition Results

Figure13 shows the result of image recognition after segmentation. The blue and black parts of the result represent the recognized farmland. The difference of color is the recognition of different kinds and different crop colors. Generally speaking, the classification results of this method are in line with the actual farmland distribution and only in the right mountainous area with complex vegetation coverage has a greater impact on the classification results. According to statistics, ER, NR, and SD are 15.6%, 12.4%, and 76.5%, respectively. The area statistics of crops classified in this paper are as follows: AS=14320.7m2. Artificial interpretation of the crop area is 17595.4 m2, calculated the accuracy of farmland area AC=81.3%. Statistical work was carried out on crop regions of remote sensing images to meet the pilot application requirements of agricultural remote sensing center.Figure 13
Farmland recognition results from segmented images.


## 6.4. Contrast of Image Recognition Indicators

As can be seen from Table1, the classification results obtained by multifeature decision-making in this paper are superior to those obtained by single feature in both error rate and similarity, and the leak rate also shows better performance. The ER value of ρB, the NR value of ρG, and the SD value of ρNIR are the highest. Overall, it is better than a single feature. The real crop area in the classification result is obtained by multiplying the number of pixels of the real crop and the spatial resolution of the image. Spatial resolution refers to the minimum distance between two adjacent objects that can be recognized in remote sensing images. The accuracy of the classification method in this paper is AC=81.3%, which basically identifies the area of farmland accurately. As an approximate solution, it can almost fully perform its functions. As can be seen in Figure 14, the leakage rate of the three gamuts is relatively low, which proves that the segmentation can be basically accurate in the three gamuts.Table 1
Classification evaluation table for different gamut.
ER (%)NR (%)SD (%)ρR37.915.362.5ρG56.310.661.7ρB80.17.352.6ρNIR33.68.368.4Figure 14
Comparison of different segmentation rates of three gamuts.In order to prove the validity of this method in extracting agricultural targets from GF-1 satellite remote sensing images. Under the combination of the same feature spaceρR, ρG, ρB, ρNIR, CON, ENT, IDM, NDVI, DVI, and PVI, three methods of classification regression tree (CART), SVM (support vector machine) and random forest are used to classify the same region. The three indicators of statistical error rate, missing rate, and similarity are shown in Table 1.Table2 shows that the classification method in this paper is usually better than other methods, with the highest difference and similarity, and the lowest leakage rate, because the function selection is accurate. Because of the influence of target extraction, the features selected in this paper are not enough to fully reflect the target information, and the interaction between features is not considered. Classification regression tree needs manual feature threshold setting, but threshold setting directly affects classification accuracy, but threshold setting based on manual experience usually does not achieve the best classification effect, and is not suitable for other scenarios. As a commonly used classification algorithm in engineering, SVM is also inferior to this method in dealing with agriculture with high spectral complexity. Random forest algorithm has fewer parameters and does not need to standardize its functions. The classification results are close to this method. But the overall indicators are still not as good as the methods in this paper.Table 2
Comparison of classification algorithms.
ER (%)NR (%)SD (%)Classification and regression tree (CART)30.28.067.5SVM (support vector machine)20.413.570.6Random forest12.59.780.1Multifeature classification method4.25.695.8


## 7. Conclusions

In order to extract different crops, the classification of crops and noncrops is the key step of crop extraction. The crop/noncrop region is modeled by using hypersegmentation technology to get superpixels. Sparse representation is applied to different images. Finally, in order to cut crops, the multifunctional decision-making voting results are applied to the classification results of image blocks. Finally, the statistical work of crop/noncrop regions is extracted and completed. Experiments show that the proposed detection and recognition algorithm can effectively extract the target of agricultural ecological crops in real time. In this paper, in order to complete the information management of remote sensing image and the superpixel modeling of extracting target, an agricultural environment detection system based on image processing is established. The system can be modified in real time as needed, can be extended to other crops, and is highly suitable for agricultural system testing in a variety of complex environments. At the same time, when classifying some complex environments, some specific environmental feature information is included in the environmental information. Users can choose different classification thresholds according to actual use needs and ultimately determine different classification methods. At the same time, other classification algorithms can be used. The classification algorithm in this paper can achieve real-time and accurate classification detection of different crops and plays an irreplaceable role in the detection of agricultural ecological environment.



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*Source: 4235341-2022-04-09.xml*