## 5 Semantic Understanding of Concept Layer
### Feature-Learning-Based Method
#### 5.3.3 The Method Based on Weak Supervised Deep Learning  
In HSR remote sensing image scene classification tasks, weak supervision usually uses labeled samples similar to target samples to train scene classification models. This method divides the dataset into the source domain and target domain. The former is different from the latter but similar. The latter can obtain labels through various transfer learning and further be used for training scene classification models. [PERSON] et al. [148] took the features extracted from labeled images as the source domain, and the features extracted from unlabeled images as the target domain. Then, apply them to network training and optimize the specified loss function to classify labeled and unlabeled data. [PERSON] et al. [149] further improved deep structural metric learning (DSML) by proposing Diversity-Promoting-DSML (D-DSML), which reduces the parameter redundancy produced by DSML and improves the feature representation ability. Some existing deep learning classification tools include OverFeat [150], DeCAF [151], Caffe [152], AlexNet and so on (Figure 7). However, in these models, learning millions of network parameters also requires millions of training data as input. In order to reduce the over-fitting problem, a smaller network structure can be constructed. However, the generalization ability of the network model trained by this method is limited, such as gradient enhancement convolutional neural network [153] and multi-perspective convolutional neural network [154]. Therefore, the unsupervised feature-learning method directly uses the network model trained on the data set of images as the feature extractor to extract the deep features of the image directly, or after the feature transformation is input into the classifier for classification, higher classification results could be obtained [155]. Furthermore, the stacked covariance pooling method transforms the extracted multi-layer convolution layer features to obtain the global deep features of the image, which can effectively fuse the multi-layer deep features [156]. The method of deep feature fusion can further improve the accuracy of geographic scene classification. The most direct way is to cascade the features of the fully connected layers extracted from different network models [158]. In addition, discriminant correlation analysis (DCA) can be used to fuse the features of different fully connected layers [159]. Alternatively, the classification results of multiple models are fused based on Choquet fuzzy integral [160]. The UC-Merced dataset is one of the classic open resource sets for classification tasks of geographic scenes. Using the UC-Merced dataset as experimental data, the performance of existing geographic scene classification models is summarized (Table 4). Figure 7: Architectures of different ConvNets evaluated in [157]. Purple boxes indicate the layers from where features were extracted in the case of using the ConvNets as feature extractors. (**a**) PatreoNet; (**b**) AlexNet; (**c**) CaffeNet; (**d**) VGG16; (**e**) OverFeats; (**f**) OverFeat\({}_{L}\). Although great progress has been made in geographic scene classification using deep learning algorithms, compared with the shallow algorithm, the classification effect has been improved obviously. However, the application of deep learning still faces many problems, such as the following:  
1. In terms of training data, the success of a deep neural network is that it can fit large-scale samples without sacrificing generalization ability. In the field of geographic