## 3 Remote Sensing Image Scene Understanding
### Deep-Learning-Driven Scene Classification  
The deep convolution neural network [26] has achieved promising results in the field of natural image scene classification, and it also has many similarities with remote sensing image scene classification. Through the migration of the data set training model, the classification accuracy of the remote sensing image dataset can also be very encouraging. However, there are only three channels of red, green, and blue in natural images, while remote sensing image data with more than three bands have not yet been able to be trained in deep learning training networks, such as ImageNet. This section mainly introduces the pre-trained convolutional neural network model, the unsupervised classification method of scene classification, and fully supervised deep feature networks. The typical classification process is shown in Figure 2. After the input original image passes through the convolution layer, the regularization and pooling layer, the convolution operation, the full connection layer, and a normalization and pooling operation, a classifier is used to get a classification result. The most commonly used classifiers are: the support vector machine (SVM), AdaBoost [27], k-nearest-neighbor [28] (kNN), conditional random field [29] (CRF), sparse representation-based classification [30] (SRC), artificial neural network [31] (ANN), random forest, Markov random field (MRF), and support tensor machine [32] (STM).  
Typical deep learning network structures include the deep belief network which needs to vectorize the raw image and can lead to loss of topology information, the stacked autoencoder (SAE) [33], and convolutional neural networks (CNNs). For a large number of labeled sample datasets, convolutional neural networks (CNNs) is the most effective learning model for feature extraction. The basic network structure of feature learning is the convolution layer, normalization, pooling layer, and full connection layer [34]. As mentioned above, deep learning has incomparable advantages over traditional algorithms. Scene classification mainly includes artificial features (Grey-Level Co-occurrence Matrix (GLCM) [35], Local binary patterns (LBP) [36], Histogram of Oriented Gradient (HOG) [37], Gist [38]), data-driven supervised classification features, and data-driven unsupervised classification features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Land-Use Classes** & **Volume** & **Size** & **Resolution (m)** & **Year** \\ \hline UC-Merced [6] & 21 & 2100 & \(256\times 256\) & 0.3 & 2010 \\ WHU-RS19 [19] & 19 & 1005 & \(600\times 600\) & Up to 0.5 & 2010 \\ RSSCN7 & 7 & 2800 & \(400\times 400\) & - & 2015 \\ RSC11 & 11 & 1232 & \(512\times 512\) & 0.2 & 2016 \\ SIRI-WHU [20] & 12 & 2400 & \(200\times 200\) & 2 & 2016 \\ AID [21] & 30 & 10,000 & \(600\times 600\) & 0.5–0.8 & 2017 \\ NWPU-RESIC45 [2] & 45 & 31,500 & \(256\times 256\) & 0.2–30 & 2017 \\ PatternNet [22] & 38 & 30,400 & \(256\times 256\) & 0.062–4.693 & 2017 \\ RSI-CB128 [23] & 45 & \textgreater{}36,000 & \(128\times 128\) & 0.3–3 & 2017 \\ RSI-CB256 & 35 & \textgreater{}24,000 & \(256\times 256\) & 0.3–3 & 2017 \\ AID++ [24] & 46 & \textgreater{}400,000 & \(512\times 512\) & - & 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introduction of several typical scene databases.