# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Discussion  
This subsection discusses the classification accuracies of classes from the two datasets and also elaborates on the comparison of the proposed framework with the state-of-the-art methods. From the Indian Pines data, it was observed that there are 6 types of crops, Corn-notill, Corn-mintill, Corn, Soybean-notill, Soybean-mintill, and Soybean-clean, with class numbers 2, 3, 4, 10, 11, and 12. The robust implementation of the classification method resulted in accuracies of 96.64%, 97.12%, 96.67%, 97.94%, 99.67%, and 95.97%, respectively. The accuracy range was found to be 95.97-99.35%. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Indian Pines**} & \multicolumn{2}{c}{**Salinas**} \\ \hline
**Class ID** & **Accuracy** & **Class ID** & **Accuracy** \\ \hline
1 & 0.6667 & 1 & 1.0000 \\
2 & 0.9664 & 2 & 0.9388 \\
3 & 0.9712 & 3 & 0.9453 \\
4 & 0.9667 & 4 & 1.0000 \\
5 & 1.0000 & 5 & 0.9925 \\
6 & 0.9891 & 6 & 0.9970 \\
7 & 0.9000 & 7 & 0.9966 \\
8 & 1.0000 & 8 & 0.9411 \\
9 & 1.0000 & 9 & 0.9974 \\
10 & 0.9794 & 10 & 0.9817 \\
11 & 0.9935 & 11 & 0.9652 \\
12 & 0.9597 & 12 & 0.9751 \\
13 & 1.0000 & 13 & 1.0000 \\
14 & 0.9937 & 14 & 0.9366 \\
15 & 0.9485 & 15 & 0.9004 \\
16 & 1.0000 & 16 & 0.9912 \\ AA & 95.84\% & AA & 97.24\% \\ OA & 97.62\% & OA & 96.08\% \\ Execution Time (in & & Execution Time (in & 1024.35 \\ seconds) & & seconds) & 1024.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Details of class-wise accuracy of Indian Pines and Salinas data. Figure 8: Indian Pines data (**a**) Ground truth (**b**) Category label (**c**) Crop classification result on entire data (**d**) Crop classification result. Figure 9: Salinas data (**a**) Ground truth (**b**) Category label (**c**) Crop classification result on entire data (**d**) Crop classification result. From the Salinas data, it was observed that there are 6 types of crops, Fallow, Fallow,_rough_plow, Fallow_smooth, Lettuce_romaine_4 wk, Lettuce_romaine_5 wk, and Lettuce_romaine_6 wk, with class numbers 3, 4, 5, 11, 12, and 13. The robust implementation of the classification method resulted in accuracies of 94.53%, 100%, 99.25%, 96.52%, 97.51%, and 100%, respectively. The accuracy range was found to be 94.53-100%. The proposed method for HSI classifications was compared with four state-of-the-art methods, including 3 DGSVM [52], CNN-MFL [53], SS3 FC [54], and WEDCT-MI [30], to prove the effectiveness in classifying the different crop regions. The first method used for the comparison is the integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification called 3 DGSVM [52]. In this work, more importance was given to spatial information. 3 DDWT is used to extract spatial features. Probabilistic SVM coupled with MRF-based post-processing was used for HSI classification. The second method used for the comparison is Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning called CNN-MFL [53]. In this work, multiple features were extracted first, followed by several CNN blocks for each set of features. Here, geometric features were incorporated using attribute profiles. This is a novel technique that takes advantage of multiple feature learning and CNN to perform accurate HSI classification. The third method used for the comparison is Spectral-Spatial Exploration for Hyperspectral Image Classification via the Fusion of Fully Convolutional Networks called SS3 FC [54]. This method used spectral, spatial, and semantic information along with Fusion of Fully Convolutional Networks for HSI classification. A novel technique for the balanced splitting of the training/test dataset was introduced to solve the insufficient training samples problem. The fourth method used for the comparison is unsupervised band selection based on weighted information entropy and 3D discrete cosine transform for hyperspectral image classification called WEDCT-MI [30].