###### Abstract  
As one of the primary means of Earth observation, high-spatial-resolution remote sensing images can describe the geometry, texture and structure of objects in detail. It has become a research hotspot to recognize the semantic information of objects, analyze the semantic relationship between objects and then understand the more abstract geographic scenes in high-spatial-resolution remote sensing images. Based on the basic comnotation of geographic scene understanding of high-spatial-resolution remote sensing images, this paper firstly summarizes the keystones in geographic scene understanding, such as various semantic hierarchies, complex spatial structures and limited labeled samples. Then, the achievements in the processing strategies and techniques of geographic scene understanding in recent years are reviewed from three layers: visual semantics, object semantics and concept semantics. On this basis, the new challenges in the research of geographic scene understanding of high-spatial-resolution remote sensing images are analyzed, and future research prospects have been proposed. e 2  
it has been applied to urban planning, disaster management, environmental monitoring, military activities and many other fields [3; 4]. As a collection of multiple objects and their surroundings in the real world, understanding the semantics of scenes is an important task in remote sensing image interpretation. Scene understanding is based on the perception of remote sensing image data, combined with visual analysis, image processing, pattern recognition and other technical means, to mine the characteristics and patterns in the image from different levels such as computational statistics, behavioral cognition and semantics, so as to realize the effective analysis, cognition and representation of the scene. However, due to the limitations of space imaging technology, HSR remote sensing images, although of higher spatial resolution, are relatively deficient in spectral information [5]. In HSR remote sensing images, the spectral heterogeneity of the same type of ground objects is enhanced, and the spectral diversity of different ground objects is reduced, which leads to the decline in statistical separability of different types of ground objects in the spectral domain [6]. Therefore, understanding the geographic scenes in the HSR remote sensing images includes the identification of both objects and the relationships between objects, as well as the analysis of themes categories with richer concepts and content implied in the geographic scene. Because of the complexity and intersection of these tasks, the research on geographic scene understanding of HSR remote sensing images still faces many challenges, mainly including the following three aspects:  
1. In terms of the basic principles of geographic scene understanding, the machine will identify the objects or targets contained in the scene according to the similarity of image data. In contrast, humans analyze the semantic information of scene content through the category and spatial distribution of ground objects, and form high-level features through abstract concepts [7]. There is a semantic gap between the conceptual similarity of human understanding and the digital storage form similarity of machine identify. This makes it impossible to relate low-level visual features (such as color, shape, texture, etc.) to high-level semantic information directly. 2. In terms of the data characteristics of HSR remote sensing images, the improved spatial resolution makes the ground objects in the images have more fine texture features, more obvious geometric structure and clearer location layout. Correspondingly, it also aggravates the difficulty of data processing in intelligent image interpretation. In high resolution images, the spectral heterogeneity of similar objects is enhanced, and the spectral difference of different objects is reduced. This leads to a decrease in the statistical separability of different ground objects in the spectral domain [8].