## 3 Target Discrimination
### SVM Training  
The main aim of the SVM classification is to discriminate the real ship targets from candidates based on different features. We have already obtained the texture description in Section 3.1. In addition, we use the other 10 feature descriptions, including shape description, contrast and texture description, and invariant moment features. The shape feature includes length, width, area, and the perimeter of the minimum external moment. Furthermore, we selected three morphological features to eliminate false alarms, including length-width ratio, compactness, and rectangularity, which are calculated as follows:  
\[\mathrm{RH}=\mathrm{H/W} \tag{28}\]  
\[\mathrm{RT}=\mathrm{P}^{2}/\mathrm{S} \tag{29}\]  
\[\mathrm{RR}=\mathrm{S}/\left(\mathrm{H}\times\mathrm{W}\right) \tag{30}\]  
where H and W are the length and width of the minimum external moment in pixels; P is the perimeter of the contour in pixels; S is the number of the connected area points in pixels. At the same time, we analyzed the texture characteristics of a large number of ships and non-ships. The texture of waves and clouds changed slowly, and that of ships varied greatly. Therefore, we introduced the histogram variance of MLBP (proposed by Section 3.1) and the correlation and contrast of the grey-level co-occurrence matrix (GLCM) [23] to strengthen the ability to distinguish ships and non-ships. The contrast and correlation of the GLCM are calculated as follows. \[\mathrm{M\_contrast}\ =\sum\limits_{i}\sum\limits_{j}{(i-j)^{2}P_{ij}} \tag{31}\]  
\[\mathrm{M\_correlation}=\sum\limits_{i}\sum\limits_{j}{P_{ij}\frac{(i-\mu)(j- \mu)}{\sigma^{2}}} \tag{32}\]  
where \(P_{ij}\) is an element of GLCM; \(\mu\) is the average of GLCM, \(\mu=\sum\limits_{i}\sum\limits_{j}{i\cdot P_{ij}}\)\(\sigma^{2}\) is the standard deviation, \(\sigma^{2}=\sum\limits_{i}\sum\limits_{j}{(i-\mu)^{2}P_{ij}}\). The contrast reflected the degree of the depth of the texture. The lighter the texture groove, the lower the contrast, and the more blurred the visual effect is. The correlation measured the degree of similarity between the spatial GLCM elements in the row or column direction. If that value of the matrix is equal in size, the value of the correlation value is small. Figure 9: One example of the MLBP description process. (**a**) The original image. (**b**) A contrast map calculated using Equation (22) (**c**) The contrast level calculated using Equation (23). (**d**) Initial binary description 00000101. We randomly extracted 20 samples (as shown in Figure 10) from the dataset for statistical analysis of the correlation and contrast of GLCM and the histogram variance of MLBP, as shown (a), (b), and (c) in Figure 11. The resolution of images from the satellites GF-2 and ZY-3 was 2 m. We can see that the characteristics of ships are distinguished from those of clouds and waves. This is because of the fact that the internal texture of ships is quite different compared with that of clouds and waves. Figure 11: The results of feature statistical experiments. (**a**) The contrast of grey-level co-occurrence matrix (GLCM) feature statistics. (**b**) The correlation of GLCM feature statistics. (**c**) The histogram variance of MLBP feature statistics. (**d**) The Hu moment feature statistics. Figure 10: Some typical ship and non-ship targets involved in the statistics. The Hu moment is a highly concentrated image feature with translation, rotation, and scale invariance. The first two moments M1 and M2 of Hu were used as a set of parameters to describe the characteristics of ships. We rotate the 20 ships by 5\({}^{\circ}\), and the second moment of Hu changes little after the rotation, as shown (d) in Figure 11 From the above analysis, it can be seen that the feature description selected in this paper is strong and robust. We used the SVM to discriminate real ship targets from candidates based on the obtained features. Eleven features should be feature-transformed and mapped to a high-dimensional space.