# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Results with Accuracy Measures  
In this work, we used both quantitative and qualitative result analysis to show the performance of the proposed approach for crop classification. Three standard evaluation quantitative metrics were used [42] and are defined as:  
* Overall Accuracy (OA): The percentage of correctly labeled pixels in the crop classification;
* Average Accuracy (AA): Average percentage of correctly labeled pixels for each crop;
* Class-wise Accuracies (CA): Percentage of correctly labeled pixels in each crop.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Param \#** \\ \hline conv2d\_10 (Conv2D) & (None, 5, 5, 64) & 10,432 \\ conv2d\_11 (Conv2D) & (None, 5, 5, 128) & 73,856 \\ dropout\_5 (Dropout) & (None, 5, 5, 128) & 0 \\ flatten\_5 (Flatten) & (None, 3200) & 0 \\ dense\_23 (Dense) & (None, 256) & 819,456 \\ dense\_24 (Dense) & (None, 128) & 32,896 \\ dense\_25 (Dense) & (None, 64) & 8256 \\ dense\_26 (Dense) & (None, 32) & 2080 \\ dense\_27 (Dense) & (None, 16) & 528 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CNN Model used for Indian Pines data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.No** & **Parameter Type** & **Value** \\ \hline
1 & Loss Function & Categorical cross entropy \\
2 & Model & Sequential \\
3 & Epochs & 100 \\
4 & Optimizer & Adam \\
5 & Batch size & 2**3 \\
6 & Convolution Layer: filters\_1 & 2**6 \\
7 & Convolution Layer: filters\_2 & 2**7 \\
8 & Dropout rate & 0.35 \\
9 & Fully Connected Layer:Units\_1 & 2**8 \\
10 & Fully Connected Layer:Units\_2 & 2**7 \\
11 & Fully Connected Layer:Units\_3 & 2**6 \\
12 & Fully Connected Layer:Units\_4 & 2**5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected Parameters for CNN Model for two data sets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Param \#** \\ \hline conv2d\_10 (Conv2D) & (None, 5, 5, 64) & 10,432 \\ conv2d\_11 (Conv2D) & (None, 5, 5, 128) & 73,856 \\ dropout\_5 (Dropout) & (None, 5, 5, 128) & 0 \\ flatten\_5 (Flatten) & (None, 3200) & 0 \\ dense\_23 (Dense) & (None, 256) & 819,456 \\ dense\_24 (Dense) & (None, 128) & 32,896 \\ dense\_25 (Dense) & (None, 64) & 8256 \\ dense\_26 (Dense) & (None, 32) & 2080 \\ dense\_27 (Dense) & (None, 16) & 528 \\ \hline \hline \end{tabular}
\end{table}
Table 5: CNN Model used for Salinas data.  
The OA, AA, and CA values of Indian Pines and Salinas, along with execution time, are shown in Table 6.  
The qualitative metric used in this work is the classification map. Figure 7 shows the classification map of the Indian Pines data, and Figure 8 shows the classification map of the Salinas data [49; 50; 51].  
For Indian Pines data, the highest classification result was obtained for two crop classes: (Corn-mintill: class number \"3\") with an accuracy of 97.12%, and (Soybean-mintill: class number \"11\") with an accuracy of 99.35%. These two crop class regions are circled in Figure 8d of the classification map result.  
For Salinas data, the highest classification result was obtained for two crop classes: (Fallow_rough_flow: class number \"4\") with an accuracy of 100%, and (Lettuce_romaine_6 wk: class number \"13\") with an accuracy of 100%. These two crop class regions are pointed out in Figure 9d of the classification map result.