## 5 Semantic Understanding of Concept Layer
### Object Semantics Based Method
#### 5.2.2 Local Semantics Based Method  
To avoid the process of object detection and recognition, the HSR remote sensing image can be divided according to rules and the local image descriptors of each sub-block can be extracted. The correspondence between local descriptors and local semantic concepts is established, and the scene classification is completed by using the probability distribution of local semantic concepts. There are two main algorithms based on local semantic concepts: the probabilistic topic-model-based method and the bag-of-visual-words-model-based method. Because the feature of spatial structure expresses the relationship between objects, it does not exist independently. Therefore, this feature is often used in conjunction with the bag of visual words model or the probabilistic topic model to enhance semantics. (1) Bag of visual words model  
The visual codebook is defined in advance, and the image content is described by the probability distribution of the appearance of the visual codewords. Then, the geographic scenes are classified according to the probability distribution. In the process of constructing the bag of visual words model, feature extraction, visual dictionary learning, feature mapping and whether to add spatial context information all have an impact on the classification results [106]. In the aspect of feature extraction, we consider the construction of multi-feature scenes in low-dimensional space under different perspectives and use feature complementarity to carry out feature fusion to solve the problem of dimension reduction from a multi \begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Advantage** & **Disadvantage** & **Applicable** \\ \hline \multirow{2}{*}{Object Bank} & Identifiable targets and natural scenes & High computational complexity and high feature dimension & Natural scenes with landmark targets \\ \hline \multirow{2}{*}{Latent Pyramidal Regions} & Good performance for regions & \multirow{2}{*}{Focus on the shape structure of the scene, lack of deep semantic understanding} & \multirow{2}{*}{Scenes with complex background and crowded targets} \\  & with specific structures & & \\ \hline \multirow{2}{*}{Bag of Parts} & Good performance for areas with boundaries or corners & & \\ \hline \multirow{2}{*}{Latent Semantic Analysis} & The synonym is characterized by dimensionality reduction, and the redundant data are used & Polysemous words have low discrimination and high computational complexity & Scenes with heterogeneous information and clear boundaries \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of main methods in target recognition. perspective [107]. Existing visual dictionary learning includes generation (unsupervised) and discriminant (supervised) methods [108; 109]. After visual dictionary learning, the image local descriptor is mapped to the visual dictionary. The modification of the mapping method can improve the representation of local semantics [110]. In addition, feature description can be incorporated into feature space partitioning to capture the high-order structure inherent in the scene [111]. This makes the local semantics have both local gradient information, local structure information [112] and global spatial information [113; 114; 115]. (2) Probabilistic topic model  
The scene semantic content is first modeled by probability distribution based on the codewords. Then, the latent semantic topics in the images are learned by using the probability distribution model. In addition, the geographic scenes are classified according to the probability distribution of latent semantic topics. Semantic topic modeling includes the generative probabilistic model and the discriminative probabilistic model. The generative probabilistic model of the geographic scene is constructed according to the joint probability distribution of the scene category in the feature space, using pLSA [116], LDA [117] and improved LDA (ts-LDA, css-LDA) to mine the latent semantic information of visual words [118].