## 4 Feature Extraction Based on the CAE Model  
In this paper, the feature extraction and data dimension reduction of remote sensing image objects are carried out using the CAE; however, the number of extracted feature maps is difficult to determine and has a crucial impact on the classification results. In this case, we set the number to 12 to ensure that the data quality of the feature maps remains consistent with the original image. Figure 3 shows that an information redundancy phenomenon occurs between feature maps. Therefore, 12 feature maps should be grouped to eliminate redundant information and determine a more reasonable number of feature maps. The redundancy elimination method is as follows:  
In the central area of a 53 \(\times\) 53 matrix of feature maps, the texture features of each feature map are compared and analysed based on entropy, energy, and inverse difference moment indicators derived from a grey-level co-occurrence matrix [32; 33; 34]. Sample 177 and its feature maps are shown as an example in Figure 3 below. Figure 3: Example of feature maps (the red box in feature map 2) represents a central 53 \(\times\) 53 grid). Figure 2: Architecture of the CAE_CNN model (the green layers represent the feature maps obtained by convolution operations, and the yellow layers represent the feature maps obtained by max pooling operations). The change in each indicator is shown in Figure 4.  
If the entropy value difference is less than 0.1, the maps are considered similar and can be grouped together. According to the graph, the entropy of feature maps 1, 4, and 12 is similar; therefore, the maps can be grouped together. The entropy values of feature maps 3, 5, 6, 7, and 9 are also similar, so these maps can be aggregated into a second group. The difference in entropy values of the other feature maps is greater, thus, each of these maps represents its own group. Therefore, there is information redundancy among 12 feature maps, and it is more effective to use 6 sets of feature maps instead of 12 feature maps. Next, based on the grey histogram (Figure 5) of each feature map, the information contained in each feature map is described. The entropy of feature maps 2 and 8 is 1.70 and 1.19, respectively. The energy of these feature maps is 0.31 and 0.47, respectively. The inverse difference moment of the feature maps is 0.93 and 0.94, respectively. These differences indicate that feature maps 2 and 8 have very rich and unique  
Figure 4: Variation of each indicator. Figure 5: Grey histograms ((**a**–**f**) represent the grey histograms of feature maps 1, 2, 3, 8, 10, and 11, respectively. The grey histograms of feature maps 5, 6, 7, and 9 are similar to the grey histogram of feature map 3; and the grey histograms of feature maps 4 and 12 are similar to the grey histogram representing feature map 1.). texture information. The grey histogram also shows that they have different spectral features, which are beneficial to classify. The entropy of the feature maps numbered 1, 4, and 12 is almost 0, while the energy and inverse difference moments of these are close to 1, which indicates that the local areas of these maps lack variability and have no texture information. The grey histograms (4, 12, and 1 are similar) show that, in addition to the black background and the subject, some sporadic grey points constitute the subject's contour, revealing obvious shape characteristics that are beneficial to classify. The entropy, energy, and inverse difference moments of feature maps 3, 5, 6, 7, and 9 range from 0.75-0.85, 0.50-0.55, and 0.965-0.982, respectively. Although there is no texture information on the subject, where the external edge of the subject's silhouette forms a point cloud through the convolution operation with rich texture information, the grey histograms (5, 6, 7, 9, and 3 similar) show that there is a clear distinction between the main body and the background. These features also have shape characteristics that are helpful to classify.