## 3 Remote Sensing Image Scene Understanding
### Deep-Learning-Driven Scene Classification
#### 3.2.1 Feature from Pre-Trained CNN for Remote Sensing Image Scene Classification  
As depicted in [26], the authors utilized the pre-trained CNN network model based on ImageNet, which performs well on scene-level datasets, such as The University of California, Merced (UC Merced) Land-use and Brazilian Coffee Scenes, and they also used the conservative SVM. The paper depicts that in different scene-level datasets, the scene-level aerial image datasets often achieve better results than agricultural datasets. The reason for this is because many objects in the data set of aerial images (e.g., aircrafts, cars) have similar salient edges and boundaries with the datasets pre-trained in the CNN network. Thus, training samples have a great influence on the accuracy of scene classification in supervised classification. The proposition in [39] also used pre-trained CNN models and domain-specifically fine-tuned, pre-trained CNN models on the scene-level dataset, which functioned as universal feature extractors to extract richer high-level representation features. In [39], the writer adopts two strategies for feature extraction. The first is that the parameters of the convolutional neural network model are trained on an ImageNet dataset, and the results are migrated directly to a scene classification task. That is to say, the last classification layer is removed, and the feature output vector of the former layer is used as the input vector of the latter layer. The second strategy is trained on the ImageNet dataset as before, but through fine-tuning in a specific domain. Owing to fine-tuning, the average classification accuracy is further boosted by 2-5% for AlexNet, a 16 layer VGGNet, and GoogleNet, respectively. A Discriminative CNN (D-CNN) model is proposed in [40], which optimizes the discriminant objective function and introduces a metric learning regularization term into the existing CNN model features, in addition to minimizing classification errors. Through testing on the UC Merced, Aerial Image Dataset (AID), NorthWestern Polytechnical University-REmote Sensing Image Scene Classification (NWPU-RESISC45) datasets, it has been found that this method can reduce classification errors caused by the similarity between classes. Even though the training ratio is only 20%, the overall accuracy can reach 90.82 \(\pm\) 0.16% and 89.22 \(\pm\) 0.50% on the aforementioned AID and UC Merced dataset, respectively. The paper [41] puts forward a gradient-boosting random convolutional network (GBRCN). This is the first time that a deep integration framework for scene classification is proposed, and it introduces a multiclass soft-max loss function into the framework. The basic classifier is Random CNet (RCNet), which can reuse the weight of each convolution model and reduce the parameters to make the feature extraction process more efficient. Compared with traditional methods, such as Spatial Pyramid Matching Kernel (SPMK) (89.67%), SIFT + Sparse Coding (SSC) (91.33%), Saliency-guided Sparse Auto Encoder (SSAE) (92.20%), the final classification accuracy can reach 98.78%. Unlike most methods which concentrate on deep network architectures, [42] focuses more on feature fusion methods, where Visual Geometry Group (VGG)-Net is still used for feature  
Figure 2: Typical architecture of a convolutional neural network (CNN) for scene classification. The input image gets feature representation through deep networks (pre-trained CNN, unsupervised deep feature learning, and an end-to-end architecture can be included). extraction, and the full connection layer obtained from the deep learning network model is considered to be a separate feature description method. Unlike the traditional feature fusion methods based on addition and connection, discriminant correlation analysis (DCA) is used as a feature fusion method. This fusion method can describe scene-level images, reduce dimension and data information, and achieve better results for Very High Resolution (VHR) images than traditional images.