# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 2 Materials and Methods  
Crop classification using HSI consists of two steps. The first is band selection, and the second is classification. Partition-based band selection is proposed in this paper. Initially, the partition is performed based on properties of the vegetation spectrum, and three partitions are created. These are termed VIS, NIR, and SWIR partitions (Figure 1). Further bands are selected from these three partitions based on three relevant metrics, entropy, NDVI, and MNDWI, respectively. In the second step to perform classification, the concatenated bands are given as input to the designed CNN model. The CNN model consists of a series of convolution and fully connected layers. Finally, crop classification can be achieved for the selected input data. The proposed architecture for the classification of crops using HSI data is shown in Figure 2.  
Figure 1: (**a**): Spectral Reflectance Properties of Vegetation Spectrum, (**b**): Leaf cell structure showing the interaction of light with VIS, NIR, and SWIR regions of the electromagnetic spectrum. Let H be hyperspectral data with h rows, w columns and d represents number of spectral bands of H. Each denotes one band in the dataset. According to spectral reflectance properties in various regions of the electromagnetic spectrum, HSI data can be separated into n partitions Partition#1, Partition#2... Partition#n with bands d1, d2,..., dn respectively. Here:  
\[\text{d}_{1}+\text{d}_{2}+\cdots\text{d}_{\text{n}}=\text{d} \tag{1}\]  
In the present context, n value is considered as 3 with Partition#1 denoting bands in the VIS region, Partition#2 denotes bands in the NIR region, and Partition#3 denotes bands in the SWIR region. Three metrics are then chosen to select bands from each partition. Information Entropy (IE) is a criterion to measure spatial information in the HSI bands. For a particular band X\({}_{\text{j}}\), Information Entropy is defined as:  
\[\text{IE}\big{(}\text{X}_{\text{j}}\big{)}=-\sum_{\alpha\in\text{X}_{\text{j}}} \text{P}(\text{x})*\log\text{P}(\text{x}) \tag{2}\]  
where P(x) is the probability of number of grey level of the histogram of the band x in the image. Based on IE, each band in the VIS region is quantified, and top m\({}_{1}\)(\(<\) d\({}_{1}\)) bands are selected from Partition#1 based on the threshold limit value of \(\delta\)1. Normalized Difference Vegetation Index (NDVI) measures plant health in terms of greenness density, as shown in Equation (3). This is a widely used vegetation index in the remote sensing community. The NDVI ranges from +1 to \(-\)1. Dead plants have \(-\)1 as NDVI value, and healthy plants have values between 0.65 and 1. \[\text{NDVI}=\frac{(\text{NIR}-\text{RED})}{(\text{NIR}+\text{RED})} \tag{3}\]  
Based on NDVI, each band in the NIR region is quantified, and top m\({}_{2}\)(\(<\) d\({}_{2}\)) bands are selected from Partition#2 based on the threshold limit value of \(\delta\)2. Modified Normalized Difference Water Index (MNDWI) measures the open water enhanced identification and is computed using Equation (4). This will suppress noise generated by vegetation and soil and, at the same time, improve the open water features. MNDWI ranges from +1 to \(-\)1. \[\text{MNDWI}=\frac{(\text{GREEN}-\text{SWIR})}{(\text{GREEN}+\text{SWIR})} \tag{4}\]  
Figure 2: Architecture of the proposed partition-based band selection and CNN-based classification for crop classification. Based on MNDWI, each band in the SWIR region is quantified, and top \(\text{m}_{3}(<\text{d}_{3})\) bands are selected from Partition#3 based on the threshold limit value of \(\delta\)3. Here:  
\[\text{m}_{1}+\text{m}_{2}+\text{m}_{3}=\text{m and strictly }\text{m}<\text{d}. \tag{5}\]  
The proposed band selection algorithm is presented as Algorithm 1 below.