## 3 Semantic Understanding of Visual Layer  
The semantic understanding of the visual layer of the geographic scene is the extraction of basic characteristics from remote sensing image data. The essence of geographic scene understanding is to establish the mapping relationship between low-level visual features and high-level scene semantics. Thus, extracting the visual features of HSR remote sensing images is the basis of the content description of geographic scenes, which includes local features and global features. The global feature is the feature that can represent the whole image, and it has good invariance, simple calculation and intuitive representation. Common global features include color features, texture features and shape features. Among them, color features such as color sets, color moments, color correlation diagrams, color histograms [32] and color aggregation vectors [33; 34; 35] are insensitive to size and orientation and have good stability [36]. Texture features such as gray level co-occurrence matrix (GLCM) [37], the grayscale difference [38], autocorrelation function [39], gray-level run-length [40], local binary pattern (LBP) [41], etc., are characterized by local irregularity, but macroscopic regularity. Visual features cannot only describe the basic attributes of the image such as color, texture and shape, but also reflect the deep structure information of the image. In 2001, GIST was proposed by [PERSON] et al. simulating human vision to roughly extract the image and its context information [42]. GIST can extract spectral information from the image globally as its representation without segmenting the image or detecting the target in advance. GIST is simple and easy to use. However, with the increasing complexity of image content and structure, such as the analysis granularity being too coarse to ignore the details of the objects in the scene, the result of image processing is far from the correct result. In general, the global features are sensitive to the actual imaging conditions, and the robustness and generalization ability are relatively poor. The local feature can effectively resist various affine transformations and have some invariance. [PERSON] has come up with a landmark local feature descriptor, the scale invariant feature transform (SIFT), which has good scale invariance and rotational invariance [43]. Thus, SIFT is one of the most widely used features in image processing. [PERSON] et al. present an accelerated robust feature descriptor (SURF) inspired by SIFT [44]. While SURF is inferior to SIFT in scale scaling and rotational invariance, it is superior in blur and illumination variation and is several times faster than SIFT. With the advancement of research, the instability of color, light and gradient features in the process of recognition became an obstacle to image classification. The histograms of oriented gradients (HOG) feature proposed in 2005 continue the high recognition accuracy characteristic of the local feature; the gradient histogram method is used to effectively solve the problem of the low recognition rate of local scene contours due to the sensitivity of light and gradient features [45]. However, HOG features have high dimensions, low computational efficiency and great redundancy and do not consider the effect of scale transformation on classification results. The CENTRIST feature proposed by [PERSON] et al. in 2010 solves this problem well [46]. Through the census transformation of the acquired pixels, these pixels are transformed into statistical histograms to form the CENTRIST feature to extract the object's local shape