# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 3 Dataset Description  
In the present work, experiments were conducted using two popular AVIRIS sensor-based datasets. These datasets are freely available and can be downloaded from [33]. The first hyperspectral data used is Indian Pines. These data were captured in the agricultural area of northwestern Indiana, USA, on 12 June 1992. The number of pixels is 145*145, collected in the form of 224 bands by covering the electromagnetic spectrum in the range of 400-2500 nm. Agriculture crops like corn and soya are covered in this data as 64% area. The vegetation types of grass and pastures are covered with 25% area [34; 35; 36]. The second hyperspectral data used is Salinas. These data were captured in the agricultural area of the Salinas Valley region, CA, USA, on 9 October 1998. The number of pixels is 512*217, collected in the form of 224 bands by covering the electromagnetic spectrum in the range of 400-2500 nm. Agriculture crops like vineyard fields, broccoli weeds, celery, fallow, and lettuce crops are covered with 100% area [37; 38; 39]. The class description and pixel samples information for the two datasets is shown in Table 1[40; 41; 42]. The major type of classes that exist in both Indian Pines and Salinas are shown in Figure 3. As per our interest for the present paper, different types of crops that exist in both Indian Pines and Salinas are also shown in Figure 4[43; 44; 45; 46]. The Indian Pines data belongs to the agricultural area of northwestern Indiana, USA. The area includes major portions of the Indian Creek and Pine Creek watersheds. Indiana is the tenth-largest farming state in the USA. More than 80% of the land in Indiana is dedicated to farms, forests, and woodland. The Salinas data belongs to one of the efficacious agricultural areas located in the central coast region of California, called the Salinas Valley, USA. This site is famous for producing most of the agricultural activities in the county due to its rich soil and plentiful underground water supplies [47]. The nomenclature for the individual classes is set according to the type of land, growing type, and spectral properties. For example, the classes \"Brocoli_green_weeds_1\" and \"Brocoli_green_weeds_2\" of Salinas's data belong to the same type of land cover representing broccoli weeds, but they have different spectral properties due to their different conditions. The crop \"Lettuce_romanine_4 wk\" refers to the lettuce crop that grows in the fourth week. The same terminology is used for the classes \"Lettuce_romanine_5 wk\", \"Lettuce_romanine_6 wk\", and \"Lettuce_romanine_7 wk\". From the Indian Pines data, \"Corn-notill\" represents cultivation without tillage and \"Corn-mintill\" represents cultivation with minimum tillage. The same terminology is used for the classes \"Soybean-notill\" and \"Soybean-mintill\". These two classes, along with \"Soybean clean\", represent different growing periods of the same soybean crop. More information about the data and subclass regions in both datasets is presented in Figure 5.  
On the whole, it can be concluded that the selected data is more suitable for crop classification applications. Indian Pines data consists of 64% pixel regions as different types of crops, and for Salinas data, crop regions are found to be 56%.