# An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

Authors: S. Janarthanan; T. Ganesh Kumar; S. Janakiraman; Rajesh Kumar Dhanaraj; Mohd Asif Shah
Journal: Journal of Sensors
 (2022)

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## Abstract

A significant amount of effort and cost is required to collect training samples for remote sensing image classifications. The study of remote sensing and how to read multispectral images is becoming more important. High-dimensional multispectral images are created by the various bands that show how materials behave. The need for more information about things and the improvement of sensor resolutions have led to the creation of multispectral data with a higher size. In recent years, it has been shown that the high dimensionality of these data makes it hard to preprocess them in multiple ways. Recent research has demonstrated that one of the most crucial methods to address this issue is by adopting a variety of learning strategies. But as the data gets more complicated, these methodologies are not adequate to support. The proposed methodology shows that the classification experiment using remote sensing images indicates the maximum likelihood classifier with different deep learning models; weight vector (WV) AdaBoost and ADAM can greatly limit overfitting, and it obtains high classification accuracy. Proposed VGG16 and Inception v3 increase classification accuracy along with optimization process produce 96.08%.


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## Body

## 1. Introduction

Multispectral sensors now capture the earth’s surface reflectance in hundreds of frequency bands because of the advancement in sensor technology. As a result, multispectral pictures may be used for a wide range of activities, from categorization to environmental monitoring. For example, classification accuracy is reduced when dimensionality grows and training samples are restricted, according to the Hughes phenomenon. Multispectral images may contain strong correlations between [1] neighboring and nonadjacent bands, which lowers the quantity of data that may be used for further analysis, including categorization. Accurate categorization is dependent on the extraction of certain characteristics. Band Correlation Clustering (BCC) is a new unsupervised feature extraction [2, 3] approach introduced in this paper. There are three key processes in the suggested technique, which are the bands’ correlation coefficient is calculated, the bands are clustered according to the correlation coefficient matrix, and the means of each cluster are calculated with a new methodology for feature extraction. Classification accuracy and time consumption are used to assess the support vector machine approach. [4] The resulting features are supplied into nonparametric Support Vector Machine (SVM) and parametric Machine Learning (ML), two supervised classifications, for the assessment process. [5] Comparable results are obtained using [6] unsupervised feature extraction via clustering. An evaluation of the findings demonstrates that the suggested BCC performs well in terms of computing expenses to increase the accuracy in classification steps.Object-Based Image Classification (OBIC) [7] is used to classify Very-High-Resolution (VHR) pictures. Most OBIC [8] classification algorithms use 1D features hand-crafted from picture objects (superpixels). This letter introduces a deep OBIC framework utilizing [9, 10] Convolutional Neural Networks (CNNs) to extract 2D deep superpixel features [11]. Before designing the network [12], studied superpixel mask regulations before experiments, the proposed framework for better overall accuracy, coefficient, and F-measure is delivered by our DiCNN-4 (Double-input CNN) [13] model. [14] On picture dataset than standard OBIC approaches. AdaBoost is a fantastic ensemble learning technique that combines several weak classifiers to create a strong classifier, eventually increasing the classification accuracy. [15] However, the AdaBoost combination overlooks the performance of basic classifiers at the per-class level and concentrates on their total performance. AdaBoost’s ability to enhance classification accuracy is hampered by this, [16] which makes overfitting a concern in subsequent rounds. In this paper, an enhanced [17, 18] AdaBoost algorithm with Weight Vector (WV AdaBoost) is suggested to reduce these drawbacks and preserve the advantages of AdaBoost. Each class is assigned a weight to indicate the recognition [16, 19] ability of the base classifiers using weight vectors. AdaBoost and WV AdaBoost base classifiers are trained using an Artificial Neural Network (ANN), Naive Bayes, and a decision tree [20]. The classification experiment using Remote Sensing (RS) data demonstrates that WV AdaBoost beats AdaBoost by producing much greater classification accuracy. It can greatly reduce overfitting, within a limited number of repetitions, and WV AdaBoost may increase classification accuracy to the greatest possible level.Labeling samples from each image is frequently a necessary step in the processing of multitemporal remotely sensed data, but it is a laborious and time-consuming operation. [21] The ground items frequently do not change considerably over time, thus, certain labels can be reused with the proper consistency checks. Using just one labeled picture, a new framework for weakly supervised transfer learning [22] is described in this process to categorize multitemporal remote sensing images. [23] Our system can categorize all the other multitemporal images chronologically without any labeling effort by exploiting the consistency of time-series images and a domain adaption mechanism. [24] Our system obtains a classification accuracy that is comparable to what would be obtained with [25] supervised learning. [26] With the training samples for one temporal dataset, our system is still able to handle multitemporal remote sensing images, as mentioned Figure 1 with input data, preprocessing and methodology applied, and inception v3 and VGG16 model for image classification and accuracy.Figure 1
Classification and optimization processing steps.


### 1.1. The Motivation for the Proposed Work

The main motivation for multispectral image classification and receiving better accuracy as a result compared with multistage techniques and predefined models applied to improvise the accuracy of the remote sensing data set with inception v3 and VGG16, primary goal and motivation required to analyze and improvise the accuracy with optimization techniques to increase the accuracy and computation cost reduced.


## 1.1. The Motivation for the Proposed Work

The main motivation for multispectral image classification and receiving better accuracy as a result compared with multistage techniques and predefined models applied to improvise the accuracy of the remote sensing data set with inception v3 and VGG16, primary goal and motivation required to analyze and improvise the accuracy with optimization techniques to increase the accuracy and computation cost reduced.


## 2. Related Work

### 2.1. Methodology

By utilizing the Adam optimizer and comparing it to VGG16 [27] and Inception v3, the suggested technique produce the result with excellent accuracy while requiring minimal computing time. The following model is described in detail to train a deep learning network.


#### 2.1.1. Normalization

This article analyzes remote sensing data using single-layer and deep convolutional networks. Given the enormous input data dimensionality and little labeled data, direct application of supervised (shallow or deep) convolutional networks to multi- and [28] hyperspectral imaging is problematic. The recommend combining unsupervised learning [29] of sparse features with greedy layer-wise unsupervised pretraining. [30] The technique uses sparse representations to enforce population and lifetime sparsity and to compute the logarithm of every pixel using data normalization.
(1)a=b−min∗255max−min,where b represent the grayscale value of the original image, and a represent the grayscale value after normalized image. minandmax were the grayscale level of the sample image with minimum and maximum range were 0 to 255.


#### 2.1.2. Patch Extraction

The training image set consists of image patches from the stacked covariance matrix. [31] The approach group image patches into k clusters. Random mini-batches are retrieved with 50% overlap (stride 8) and resolution determines patch sizes. High resolution requires tiny patches with the testing image set has 16×16 nonoverlapping picture patches. [32] These pure patches are labeled using the posterior probability computation. In Figure 2, input image with reference pixel and neighboring pixels with similar patches are explained.Figure 2
Patch extraction process.


#### 2.1.3. Model Training

Batch normalization and VGG16 feature extraction are used. The classification [33] layer is fully linked, batch normalized, and ReLU. The Xavier approach [34] initializes the fully connected layer’s weights. Adam uses a 0.001 learning rate and 0.00001 weight decay. This model clusters remote sensing data (GDC-SAR). In Figure 3, input image and convolution plus ReLU and max pooling resolution are reduced from 224×224×3 with 1×1×1000 with fully connected ReLU and softmax with VGG16 architecture.Figure 3
VGG 16 model process flow architecture.


#### 2.1.4. Maximum Likelihood Classifier

One of the most often used techniques for categorization in remote sensing is the maximum likelihood classifier, which places a pixel into the class that it most closely resembles. The posterior probability of a pixel belonging to classk is used to define the likelihood Lk.
(2)LK=PkK=Pk∗PX/k∑Pi∗PX/i,where, Pk is the prior probability of class k,.and PX/k is the probability density function or conditional probability to witness X from class k.∑Pi∗PX/i is likewise shared by all classes, and Pk is typically believed to be equal to each other as well. As a result, PX/k, or the probability density function, determines Lk. The probability density function is based on the multivariate normal distribution for mathematical reasons. The probability in the case of normal distributions may be stated as follows:
(3)LkX=12πn/2Σk1/2exp−12X−μk∑k−1X−μkt,where, n the number of bands, X image data of bands n, and LkX belongs to the class k. μk is the average class k vector, Σk is the covariance matrices and class k variance, and Σk is an indicator of Σk.The likelihood is the same as the Euclidean distance when the variance-covariance matrix is symmetric, and it is the same as the Mahalanobis distances when the determinants are equal.


#### 2.1.5. Inception v3

A module for Google Net, Inception v3 is a convolutional neural network that aids in object recognition and image analysis. The Google Inception Convolutional Neural Network, which was first shown at the ImageNet Recognition Challenge, is in its third iteration, In Figure4 represents the filter concatenation operation along with previous layer and convolutions layer 1x1, 3x3 and 5x5 with max pooling of 3x3.Figure 4
Inception operation with reduced dimensions.


#### 2.1.6. Adam Optimization Process

The Adam optimization algorithm’s primary goal is quicker computing [35] with a limited number of tuning parameters such as epochs, learning rate, batch size, learning rate, optimizer, and a number of neurons. Additionally, adjust the number of layers for beta 2 second moment estimations of 0.999 and near to 1.0 and beta 1 decay rates of 0.9.First, the gradient (g) with time (t) step.
(4)gt=f′Xt−1.Second, to calculate moments, moving average (m) with hyperparameter beta 1.
(5)mt=beta1∗mt−1+1−beta1∗gt.Then update the moments with moving average (v) with a second in beta 2.
(6)vt=beta2∗vt−1+1−beta2∗gt.Next, bias adjusted with correction for the 1st moment
(7)mhatt=mt1−beta1t.And then 2nd moment
(8)vhatt=vt1−beta2t.A static decay schedule is required to be applied.(9)beta1t=beta1t,beta2t=beta2t.Finally, calculate the value of the iteration of the parameter.(10)xt=xt−1−alpha∗mhattsqrtvhatt+eps,wherealpha is step size and eps are small value (epsilon) and sqrt () is the square root function.


#### 2.1.7. AdaBoost Process

Adaboost first chooses a training subset at random. By choosing the training set depending on the precision of the previous training, it iteratively trains the AdaBoost machine learning model. It gives incorrectly categorized observations at a [15] larger weight so that they will have a higher chance of being correctly classified in the upcoming round. Additionally, based on the trained classifier’s accuracy, weight is assigned to it in each iteration. [36] The more accurate classifier will be given more weight. This method iterates until the entire training set fits perfectly, or until the stated maximum number of estimators has been reached [37] to categorize the voting algorithm created for the selection.


## 2.1. Methodology

By utilizing the Adam optimizer and comparing it to VGG16 [27] and Inception v3, the suggested technique produce the result with excellent accuracy while requiring minimal computing time. The following model is described in detail to train a deep learning network.


### 2.1.1. Normalization

This article analyzes remote sensing data using single-layer and deep convolutional networks. Given the enormous input data dimensionality and little labeled data, direct application of supervised (shallow or deep) convolutional networks to multi- and [28] hyperspectral imaging is problematic. The recommend combining unsupervised learning [29] of sparse features with greedy layer-wise unsupervised pretraining. [30] The technique uses sparse representations to enforce population and lifetime sparsity and to compute the logarithm of every pixel using data normalization.
(1)a=b−min∗255max−min,where b represent the grayscale value of the original image, and a represent the grayscale value after normalized image. minandmax were the grayscale level of the sample image with minimum and maximum range were 0 to 255.


### 2.1.2. Patch Extraction

The training image set consists of image patches from the stacked covariance matrix. [31] The approach group image patches into k clusters. Random mini-batches are retrieved with 50% overlap (stride 8) and resolution determines patch sizes. High resolution requires tiny patches with the testing image set has 16×16 nonoverlapping picture patches. [32] These pure patches are labeled using the posterior probability computation. In Figure 2, input image with reference pixel and neighboring pixels with similar patches are explained.Figure 2
Patch extraction process.


### 2.1.3. Model Training

Batch normalization and VGG16 feature extraction are used. The classification [33] layer is fully linked, batch normalized, and ReLU. The Xavier approach [34] initializes the fully connected layer’s weights. Adam uses a 0.001 learning rate and 0.00001 weight decay. This model clusters remote sensing data (GDC-SAR). In Figure 3, input image and convolution plus ReLU and max pooling resolution are reduced from 224×224×3 with 1×1×1000 with fully connected ReLU and softmax with VGG16 architecture.Figure 3
VGG 16 model process flow architecture.


### 2.1.4. Maximum Likelihood Classifier

One of the most often used techniques for categorization in remote sensing is the maximum likelihood classifier, which places a pixel into the class that it most closely resembles. The posterior probability of a pixel belonging to classk is used to define the likelihood Lk.
(2)LK=PkK=Pk∗PX/k∑Pi∗PX/i,where, Pk is the prior probability of class k,.and PX/k is the probability density function or conditional probability to witness X from class k.∑Pi∗PX/i is likewise shared by all classes, and Pk is typically believed to be equal to each other as well. As a result, PX/k, or the probability density function, determines Lk. The probability density function is based on the multivariate normal distribution for mathematical reasons. The probability in the case of normal distributions may be stated as follows:
(3)LkX=12πn/2Σk1/2exp−12X−μk∑k−1X−μkt,where, n the number of bands, X image data of bands n, and LkX belongs to the class k. μk is the average class k vector, Σk is the covariance matrices and class k variance, and Σk is an indicator of Σk.The likelihood is the same as the Euclidean distance when the variance-covariance matrix is symmetric, and it is the same as the Mahalanobis distances when the determinants are equal.


### 2.1.5. Inception v3

A module for Google Net, Inception v3 is a convolutional neural network that aids in object recognition and image analysis. The Google Inception Convolutional Neural Network, which was first shown at the ImageNet Recognition Challenge, is in its third iteration, In Figure4 represents the filter concatenation operation along with previous layer and convolutions layer 1x1, 3x3 and 5x5 with max pooling of 3x3.Figure 4
Inception operation with reduced dimensions.


### 2.1.6. Adam Optimization Process

The Adam optimization algorithm’s primary goal is quicker computing [35] with a limited number of tuning parameters such as epochs, learning rate, batch size, learning rate, optimizer, and a number of neurons. Additionally, adjust the number of layers for beta 2 second moment estimations of 0.999 and near to 1.0 and beta 1 decay rates of 0.9.First, the gradient (g) with time (t) step.
(4)gt=f′Xt−1.Second, to calculate moments, moving average (m) with hyperparameter beta 1.
(5)mt=beta1∗mt−1+1−beta1∗gt.Then update the moments with moving average (v) with a second in beta 2.
(6)vt=beta2∗vt−1+1−beta2∗gt.Next, bias adjusted with correction for the 1st moment
(7)mhatt=mt1−beta1t.And then 2nd moment
(8)vhatt=vt1−beta2t.A static decay schedule is required to be applied.(9)beta1t=beta1t,beta2t=beta2t.Finally, calculate the value of the iteration of the parameter.(10)xt=xt−1−alpha∗mhattsqrtvhatt+eps,wherealpha is step size and eps are small value (epsilon) and sqrt () is the square root function.


### 2.1.7. AdaBoost Process

Adaboost first chooses a training subset at random. By choosing the training set depending on the precision of the previous training, it iteratively trains the AdaBoost machine learning model. It gives incorrectly categorized observations at a [15] larger weight so that they will have a higher chance of being correctly classified in the upcoming round. Additionally, based on the trained classifier’s accuracy, weight is assigned to it in each iteration. [36] The more accurate classifier will be given more weight. This method iterates until the entire training set fits perfectly, or until the stated maximum number of estimators has been reached [37] to categorize the voting algorithm created for the selection.


## 2.1.1. Normalization

This article analyzes remote sensing data using single-layer and deep convolutional networks. Given the enormous input data dimensionality and little labeled data, direct application of supervised (shallow or deep) convolutional networks to multi- and [28] hyperspectral imaging is problematic. The recommend combining unsupervised learning [29] of sparse features with greedy layer-wise unsupervised pretraining. [30] The technique uses sparse representations to enforce population and lifetime sparsity and to compute the logarithm of every pixel using data normalization.
(1)a=b−min∗255max−min,where b represent the grayscale value of the original image, and a represent the grayscale value after normalized image. minandmax were the grayscale level of the sample image with minimum and maximum range were 0 to 255.


## 2.1.2. Patch Extraction

The training image set consists of image patches from the stacked covariance matrix. [31] The approach group image patches into k clusters. Random mini-batches are retrieved with 50% overlap (stride 8) and resolution determines patch sizes. High resolution requires tiny patches with the testing image set has 16×16 nonoverlapping picture patches. [32] These pure patches are labeled using the posterior probability computation. In Figure 2, input image with reference pixel and neighboring pixels with similar patches are explained.Figure 2
Patch extraction process.


## 2.1.3. Model Training

Batch normalization and VGG16 feature extraction are used. The classification [33] layer is fully linked, batch normalized, and ReLU. The Xavier approach [34] initializes the fully connected layer’s weights. Adam uses a 0.001 learning rate and 0.00001 weight decay. This model clusters remote sensing data (GDC-SAR). In Figure 3, input image and convolution plus ReLU and max pooling resolution are reduced from 224×224×3 with 1×1×1000 with fully connected ReLU and softmax with VGG16 architecture.Figure 3
VGG 16 model process flow architecture.


## 2.1.4. Maximum Likelihood Classifier

One of the most often used techniques for categorization in remote sensing is the maximum likelihood classifier, which places a pixel into the class that it most closely resembles. The posterior probability of a pixel belonging to classk is used to define the likelihood Lk.
(2)LK=PkK=Pk∗PX/k∑Pi∗PX/i,where, Pk is the prior probability of class k,.and PX/k is the probability density function or conditional probability to witness X from class k.∑Pi∗PX/i is likewise shared by all classes, and Pk is typically believed to be equal to each other as well. As a result, PX/k, or the probability density function, determines Lk. The probability density function is based on the multivariate normal distribution for mathematical reasons. The probability in the case of normal distributions may be stated as follows:
(3)LkX=12πn/2Σk1/2exp−12X−μk∑k−1X−μkt,where, n the number of bands, X image data of bands n, and LkX belongs to the class k. μk is the average class k vector, Σk is the covariance matrices and class k variance, and Σk is an indicator of Σk.The likelihood is the same as the Euclidean distance when the variance-covariance matrix is symmetric, and it is the same as the Mahalanobis distances when the determinants are equal.


## 2.1.5. Inception v3

A module for Google Net, Inception v3 is a convolutional neural network that aids in object recognition and image analysis. The Google Inception Convolutional Neural Network, which was first shown at the ImageNet Recognition Challenge, is in its third iteration, In Figure4 represents the filter concatenation operation along with previous layer and convolutions layer 1x1, 3x3 and 5x5 with max pooling of 3x3.Figure 4
Inception operation with reduced dimensions.


## 2.1.6. Adam Optimization Process

The Adam optimization algorithm’s primary goal is quicker computing [35] with a limited number of tuning parameters such as epochs, learning rate, batch size, learning rate, optimizer, and a number of neurons. Additionally, adjust the number of layers for beta 2 second moment estimations of 0.999 and near to 1.0 and beta 1 decay rates of 0.9.First, the gradient (g) with time (t) step.
(4)gt=f′Xt−1.Second, to calculate moments, moving average (m) with hyperparameter beta 1.
(5)mt=beta1∗mt−1+1−beta1∗gt.Then update the moments with moving average (v) with a second in beta 2.
(6)vt=beta2∗vt−1+1−beta2∗gt.Next, bias adjusted with correction for the 1st moment
(7)mhatt=mt1−beta1t.And then 2nd moment
(8)vhatt=vt1−beta2t.A static decay schedule is required to be applied.(9)beta1t=beta1t,beta2t=beta2t.Finally, calculate the value of the iteration of the parameter.(10)xt=xt−1−alpha∗mhattsqrtvhatt+eps,wherealpha is step size and eps are small value (epsilon) and sqrt () is the square root function.


## 2.1.7. AdaBoost Process

Adaboost first chooses a training subset at random. By choosing the training set depending on the precision of the previous training, it iteratively trains the AdaBoost machine learning model. It gives incorrectly categorized observations at a [15] larger weight so that they will have a higher chance of being correctly classified in the upcoming round. Additionally, based on the trained classifier’s accuracy, weight is assigned to it in each iteration. [36] The more accurate classifier will be given more weight. This method iterates until the entire training set fits perfectly, or until the stated maximum number of estimators has been reached [37] to categorize the voting algorithm created for the selection.


## 3. Data Processing

### 3.1. Data Set

We used information from IRS P6 LISS IV remote sensing from ISRO for dataset analysis, refer tohttps://directory.eoportal.org/web/eoportal/satellite-missions/i/irs-p6. The area being studied is only the Delhi region of India, which is centered at latitude 0.00000 and longitude 75.00000 and scene center at latitude 28.615513 and longitude 77.216714, respectively. Figure 5 shows combined bands of RGB and Table 1 gives a brief description of the IRS P6 LISS IV data we used in our work. Figure 6 represents band 2 tiff image, Figure 7 represents band 3 tiff image, and Figure 8 represents band 4 tiff image.Figure 5
Combined 3 band tiff image.Table 1
Parameters of IRS P-6 LISS IV Satellite.
ParameterDescriptionParameterLISS IV dataSensorL4MXSat-IDIRS-P6Product ID142866521Sensor orientationDelhiSAR band3 bandAngle rangeLatitude 28.615513Longitude 77.216714Image formatGEOTIFFRange resolution5.8 mFigure 6
Band 2 tiff image.Figure 7
Band 3 tiff image.Figure 8
Band 4 tiff image.In Table1, it mentions about Indian remote sensing p-6 LISS IV data set description in detail.


#### 3.1.1. Data Preprocessing

The data is first level-0 preprocessed to improve quality for a more effective picture enhancement and analysis procedure. [38] Through preprocessing, binary conversion of two complex elements and these with image file formats of (.tiff extension), such that image matrix is used for patch construction and analysis, distortions are reduced and image qualities are increased. (Figures 6, 7, and 8) (Band 2, 3, and 4).Algorithm 1: Algorithm Procedure: Preprocessing.
Read: Input data (stored in the folder)Decode: Convert it into JPEG to RGB grid pixels with channelsConvert it into a floating pointInput: Neural networkRescale the pixel:Range: Between (0 : 255) to the [0,1]Output: Preprocessed tensors.Algorithm 2: Proposed Inception v3 and VGG 16 algorithm.
Source image labeled:Ys=ais,bisi=1nsTarget image unlabeled:Yt=ajtj=1ntClass labels,Yt=bjtj=1ntAssign parameters:Epoch value =100;Min-batch size: Bs =32;Learning rateα=2.0X10−4,1st and 2nd decay rate exponentialWithβ1=0.9,β2=0.999 andEpsilon:1.0X10−8Initially use VGG16 and inception v3 model trained with dataset and pre-training with CNN to initialize feature generation G and classified imagesC1,C2,C3andC4 aloneforepoch=1:num_epochdoShuffle the random source and target image data set and organize it intoNbGroups each sizemfork=1:NbdoSelect mini-batches:Ysk,YtkfromYsandYtNeed to train theG,C1,C2,C3ANDC4onYsk by optimizing (1) to (10) using Adam same is required to be done for C1 to C4 (11)End forEnd forClassify the different class target domainajtj=1nt using source and targetReturnbjtj=1nt


## 3.1. Data Set

We used information from IRS P6 LISS IV remote sensing from ISRO for dataset analysis, refer tohttps://directory.eoportal.org/web/eoportal/satellite-missions/i/irs-p6. The area being studied is only the Delhi region of India, which is centered at latitude 0.00000 and longitude 75.00000 and scene center at latitude 28.615513 and longitude 77.216714, respectively. Figure 5 shows combined bands of RGB and Table 1 gives a brief description of the IRS P6 LISS IV data we used in our work. Figure 6 represents band 2 tiff image, Figure 7 represents band 3 tiff image, and Figure 8 represents band 4 tiff image.Figure 5
Combined 3 band tiff image.Table 1
Parameters of IRS P-6 LISS IV Satellite.
ParameterDescriptionParameterLISS IV dataSensorL4MXSat-IDIRS-P6Product ID142866521Sensor orientationDelhiSAR band3 bandAngle rangeLatitude 28.615513Longitude 77.216714Image formatGEOTIFFRange resolution5.8 mFigure 6
Band 2 tiff image.Figure 7
Band 3 tiff image.Figure 8
Band 4 tiff image.In Table1, it mentions about Indian remote sensing p-6 LISS IV data set description in detail.


### 3.1.1. Data Preprocessing

The data is first level-0 preprocessed to improve quality for a more effective picture enhancement and analysis procedure. [38] Through preprocessing, binary conversion of two complex elements and these with image file formats of (.tiff extension), such that image matrix is used for patch construction and analysis, distortions are reduced and image qualities are increased. (Figures 6, 7, and 8) (Band 2, 3, and 4).Algorithm 1: Algorithm Procedure: Preprocessing.
Read: Input data (stored in the folder)Decode: Convert it into JPEG to RGB grid pixels with channelsConvert it into a floating pointInput: Neural networkRescale the pixel:Range: Between (0 : 255) to the [0,1]Output: Preprocessed tensors.Algorithm 2: Proposed Inception v3 and VGG 16 algorithm.
Source image labeled:Ys=ais,bisi=1nsTarget image unlabeled:Yt=ajtj=1ntClass labels,Yt=bjtj=1ntAssign parameters:Epoch value =100;Min-batch size: Bs =32;Learning rateα=2.0X10−4,1st and 2nd decay rate exponentialWithβ1=0.9,β2=0.999 andEpsilon:1.0X10−8Initially use VGG16 and inception v3 model trained with dataset and pre-training with CNN to initialize feature generation G and classified imagesC1,C2,C3andC4 aloneforepoch=1:num_epochdoShuffle the random source and target image data set and organize it intoNbGroups each sizemfork=1:NbdoSelect mini-batches:Ysk,YtkfromYsandYtNeed to train theG,C1,C2,C3ANDC4onYsk by optimizing (1) to (10) using Adam same is required to be done for C1 to C4 (11)End forEnd forClassify the different class target domainajtj=1nt using source and targetReturnbjtj=1nt


## 3.1.1. Data Preprocessing

The data is first level-0 preprocessed to improve quality for a more effective picture enhancement and analysis procedure. [38] Through preprocessing, binary conversion of two complex elements and these with image file formats of (.tiff extension), such that image matrix is used for patch construction and analysis, distortions are reduced and image qualities are increased. (Figures 6, 7, and 8) (Band 2, 3, and 4).Algorithm 1: Algorithm Procedure: Preprocessing.
Read: Input data (stored in the folder)Decode: Convert it into JPEG to RGB grid pixels with channelsConvert it into a floating pointInput: Neural networkRescale the pixel:Range: Between (0 : 255) to the [0,1]Output: Preprocessed tensors.Algorithm 2: Proposed Inception v3 and VGG 16 algorithm.
Source image labeled:Ys=ais,bisi=1nsTarget image unlabeled:Yt=ajtj=1ntClass labels,Yt=bjtj=1ntAssign parameters:Epoch value =100;Min-batch size: Bs =32;Learning rateα=2.0X10−4,1st and 2nd decay rate exponentialWithβ1=0.9,β2=0.999 andEpsilon:1.0X10−8Initially use VGG16 and inception v3 model trained with dataset and pre-training with CNN to initialize feature generation G and classified imagesC1,C2,C3andC4 aloneforepoch=1:num_epochdoShuffle the random source and target image data set and organize it intoNbGroups each sizemfork=1:NbdoSelect mini-batches:Ysk,YtkfromYsandYtNeed to train theG,C1,C2,C3ANDC4onYsk by optimizing (1) to (10) using Adam same is required to be done for C1 to C4 (11)End forEnd forClassify the different class target domainajtj=1nt using source and targetReturnbjtj=1nt


## 4. Results and Discussion

The three classes were used to train the model. The outcomes are also evaluated in comparison with the earlier models such as the SVM, K-means, maximum likelihood (MLC), and adaptive movement estimation. Table2 shows the effective stochastic optimization approach with accuracy levels per class expressed as percentages.Table 2
Comparison between parameters existing techniques with the accuracy level.
Classification techniques usedData set usedSoftware usedAccuracy assessmentOverall accuracyReferenceMaximum likelihood techniqueLandsat 8 satellite imageERDAS imagineKappa statistic82.5%N. A. Mahmon, et al., 2015RNNLISS IV imageMAT labKappa coefficient87.69%T. Vignesh, et al., 2021SVM (support vector machines) and CNNGoogle earth Landsat-8MATLAB R2017aOverall accuracy70.89%M. Kim et al.201873.79%CNNSouth Korea region – Google earthMATLAB R2017aOverall accuracy95.7%M. Kim et al.2018VGG16 with AdamGoogle earth and Bing mapsPythonAverage accuracy78.72W. Teng et al., [26]Proposed inception v3 with AdamLISS IVPythonAccuracyThe ultimate accuracy of a data set was assessed by averaging all of its patches. In Table3, the range of the proposed fields of examination was advantageous. It might be challenging to distinguish mixed-class urban settlements from other urban regions. This is because there aren’t many significant urban centers in these locations. The bulk of them are mixed communities, as seen in Figures 5, 6, 7, and 8. A patch may have several classes while creating the ground truth, but after categorization, it only gets one label that corresponds to the largest class. This leads to decreased accuracy along with a resolution discrepancy between SAR pictures and remote sensing data. In the above table, Adaboost is analyzed as a combined result of each band.Table 3
Comparison between new parameters with proposed Scheme.
Delhi regionSVMMLCK-means clusteringAdam optimizationVGG 16 modelInception v3 modelBand 275.6889.5276.7292.2190.8095.50Band 376.5188.7277.6593.7591.0095.80Band 468.2590.2176.6294.5292.4096.40Combined95.690.5278.3396.7293.2096.60Average79.0189.7477.3394.391.8596.08Each model and machine learning algorithm gives the best accuracy level when compared with the new deep learning model in the python environment with ImageNet and Google Net model like VGG16 and Inception v3 model produces high accuracy level of finding the above table number. In Table3, with the accuracy level of each band being analyzed with sample image data set for the classification accuracy train and test set prepared for the analysis of work and the manual operational categorization of sample class, the following hyperparameter tuning is used: Epochs 10, Batch size 16, learning rate 0.001, and input sample accuracy level 96.08. In Figure 9, it represents the proposed model.Figure 9
Comparison between existing vs. proposed multimodel accuracy.The below-mentioned test results analyze with the Google AI web application with classes 1, 2, and 3, and combined bands are being analyzed withhttps://teachablemachine.withgoogle.com/. In Figure 10, it represents the proposed workflow with deep learning model and optimization function process clearly.Figure 10
Proposed manual workflow architecture with Inception v3 and VGG16 model.In Table4, it represents the sample data along with various labeled sample classes were imported for the training and different hyperparameter tuning of values like batch size, epoch rate, and learning rate 0.0001 being trained with inception v3 and VGG16 model, by importing the input sample accuracy level and confusion matrix class and prediction rates shown in Figure 11.Table 4
User Accuracy level with sample data.
ClassAccuracySamples dataClass 11.001Class 21.001Class 31.001Figure 11
Confusion matrix.In Figure12, it represents the accuracy per epoch with testing accuracy comparison level of class 2 sample data. Figure 13 shows the accuracy per epoch class 3 sample data, and Figure 14 shows the combined data set range level showing in the graphical.Figure 12
Accuracy per Epoch class 2.Figure 13
Accuracy per Epoch class 3.Figure 14
Accuracy per Epoch class combined.In Figure15, it represents the loss per epoch combined test loss and loss range level showing in the graph.Figure 15
Loss per Epoch combined.


## 5. Conclusion and Future Scope

Deep learning is used to categorize land cover via stochastic optimization. The proposed model has a 96.08 percent accuracy rate using overlapping remote sensing picture patches. The trial findings show that the algorithm performs in line with the best practices. Hence, a general model is created, and it can be used for a variety of image data sets. Likewise, we will be able to divide the more specific classes like volume more accurately. In this research primary finding merge two deep learning model inception v3 and VGG16 with Adam optimization produce better accuracy level compare to the existing models stated in Table2, and in the future scope the segregation of mixed-class groups, it has not yet been investigated along with the deep learning model, and it can be considered for future research directions.



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*Source: 2004716-2022-10-08.xml*