# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 4 Experiment Results  
The MSTAR database [17] has 10 SAR targets, which are BMP2, T72, T62, BTR70, BTR60, 2S1, BRDM2, D7, ZIL131 and ZSU234. The images of these targets are collected at two different depression angles (15\({}^{\circ}\) and 17\({}^{\circ}\)) over a full 0\({}^{\circ}\)-360\({}^{\circ}\) range of aspect view. The experimental procedure adopted in this article follows the guideline [18], which states that classifiers using this database are to be trained using targets at the 17\({}^{\circ}\) depression angle and tested on targets at the 15\({}^{\circ}\) depression angle. Simultaneously, the targets BMP2 and T72 have different variants (BMP2-9563, BMP2-9566, BMP2-C21, T72-132, T72-812 and T72-S7), which should be included in the test set. The training set, however, only has variants BMP2-C21 and T72-132, because the classifier should recognize the variants not present in the training set. The number of each target available at the 17\({}^{\circ}\) training set and the 15\({}^{\circ}\) test set depression angles are listed in Table 1. Meanwhile, the optical images of the 10 targets are displayed in Figure 3.  
The preprocessing of the SAR images was discussed in last section. Table 2 shows the recognition rates of the three cropped images under SRC. From the table, the 80 \(\times\) 80 pixel patch indeed contains more effective information than the other patches. After unifying the sizes of the images, the unconventional methods, down-sample and Principal Component Analysis (PCA), are utilized to extract the features of the cropped images. The down-sample is achieved by bilinear interpolation here, and the output pixel value is a weighted average of pixels in the nearest two-by-two neighborhood. In comparison with the down-sample, the dimensions of the feature vector extracted by PCA are the same as the former. Meanwhile, the conventional features, baseline features [19], are adopted here to make a comparison with the unconventional features. In order to achieve excellent performance of SVM [20], the multiclass classifier, based on the one-against-one approach, adopts the feature normalization method if there is no additional stress. Simultaneously, because the different features are  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Targets** & **BMP2** & **BTR70** & **T72** & **BTR60** & **2S1** & **BRDM2** & **D7** & **T62** & **ZIL131** & **ZSU234** \\ \hline
17\({}^{\circ}\) & 233 & 233 & 232 & 256 & 299 & 298 & 299 & 299 & 299 \\
15\({}^{\circ}\) & 587 & 196 & 582 & 195 & 274 & 274 & 273 & 274 & 274 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of images of each object at different depression angles.  
Figure 3: The optical images of the 10 targets. Among them, the T62 and T72 are tanks. The BRDM2, BMP2, BTR60 and BTR70 are armored personnel carriers. The 2S1 is a rocket launcher, the D7 is a bulldozer, the ZIL131 is a truck and the ZSU234 is an Air Defense Unit.  
employed here, SVM uses a linear method to deal with down-sampling features and baseline features, and uses the Gaussian kernel method to process PCA features.