# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 1 Introduction  
Due to advancements in remote sensing image acquisition mechanisms and the growing availability of rich spectral and spatial information by using a variety of sensors, hyperspectral imaging has gained importance. In particular, Hyperspectral Image (HSI) classification has become a prominent source for practical applications in fields like agriculture, environment, forestry, mineral mapping, etc. [1; 2; 3; 4; 5]. The present paper focuses on analyzing and using HSI in the agriculture field. Accurate information about growing crops with different climate conditions and agricultural resources and with different timestamps (before, during, and after cultivation) is extremely important and useful for agricultural development. Traditional methods, like field surveys and other statistical-based analyses, are very time-consuming. Advanced remote sensingtechnology, including HSI, provides a suitable solution and can fill the gap [6; 7; 8; 9; 10] with solutions like crop classification. The problem of crop classification using hyperspectral images has been addressed by researchers with various methods [11; 12]. A method based on regression analysis was used to classify the variety of sugarcane crops in Brazil. This HSI data was captured using the EO-1 satellite [13]. The method, proposed in [14], is a combination of Support Vector Machine (SVM) and linear spectral models and was used successfully on the data captured from the Hyperion satellite. This method was also used to classify lithchi crops in Guangzhou. Crops in the Karnataka area were classified using the Spectral Angular Mapper (SAM) classifier method for the Hyperion data [15]. The HSI sensor called Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) has recently become important in the remote sensing community. AVIRIS has high spectral bands (224 bands) and spatial resolution (20 m for Indian Pines and 3.7 m for Salinas datasets) with a wavelength range of 380-2500 nm covering VIS, NIR, and SWIR regions and hence is known to be crucial for agricultural applications [16]. Some of the crop classification methods in this connection are as follows. Combined linear and nonlinear SVM algorithms were used to classify corn crops on AVIRIS data. This method obtained moderate accuracy. Soybeans and wheat crops were classified using SVM and Markov Random Field with good accuracy for the AVIRIS HSI data [17]. Unmanned Aerial Vehicle (UAV) datasets also experimented with classifying crops like cabbage, cotton, and strawberry. High accuracy was noticed using Conditional Random fields [18; 19]. Further, the Salinas data set was tested to classify different crops using a support vector machine. This method achieved a moderate level of accuracy [20]. Other methods, including spatial context support vector machines, had reasonable accuracy [21]. The above methods are insufficient to extract the required information, and it is difficult to obtain commendable results [22; 23; 24]. Several successful band selection methods have been introduced (to perform before classification) in the literature over the last few decades, including ranking-based approaches, clustering-based approaches, searching-based approaches, relative entropy, and information entropy-based approaches [25; 26; 27; 28]. The optimal clustering framework was introduced in [29] for band selection and successfully applied the novel objective function with several constraints. In these methods, bands are clustered initially and then ranked according to different measures to select the representative bands from the image. All these methods work well for selecting the informative bands and hence produce high classification accuracy. However, these methods may not be suitable for application-specific classification problems. For example, in the present context, we consider crop classification as our target. It is important to adopt the band section strategy in the view of agricultural phenology, where biophysical properties of plants are also taken into consideration.