## 3 Remote Sensing Image Scene Understanding
### Deep-Learning-Driven Scene Classification
#### Unsupervised Deep Feature Learning for Remote Sensing Image Scene Classification  
Deep neural networks [8] have been widely used to learn low-dimensional feature representations to reduce the dimensionality of VHR images. Sparse coding [43] is another famous unsupervised feature learning method, which is highly effective for scene classification when compared to the traditional Bag of Visual Words (BoVW)-based approaches [44] and generates a set of basic functions from the unlabeled data. Recently, a method combining scale-invariant-feature-transform (SIFT)-based feature descriptors and sparse coding (Sift + SC) has been put forward. Although unsupervised classification has many advantages, compared with supervised classification, it is still unable to fully automate, and has a long way to go.  
In order to overcome the influence of pre-training samples on classification accuracy and the shortcomings of given data with few labels, the unsupervised learning algorithm is also an important part of remote sensing. In [45], an unsupervised feature learning method is proposed. As we all know, one of the fatal drawbacks of unsupervised classification is that the effect of the classifier is very sensitive to the selection of parameters. The author uses a two-level feature extraction algorithm based on K-means clustering. The first layer generates a contour basis, and the second layer generates a corner basis. K-means clustering only needs to adjust one parameter, has strong robustness, and can extract complex structural features (such as corners and junctions). Using the University of California, Merced (UCM)-21 data set based on the scene level, by comparing the original training samples (83.3%) and preventing the rotation of the training samples after fitting (89.1%), the latter can usually achieve a higher level of accuracy. Therefore, in scene level classification, it is very important to use augmentation to prevent over-fitting in deep network training. The paper [46] proposed a new method for training a scene-level UC Merced dataset. [PERSON] proposed a balanced data-driven sparsity (BDDS) method, which designed a feasible sample input for a better-enforced lifetime and population sparsity (EPLS), and hence improved the supervised trained CNN. The proposed deep feature learning algorithm could overcome difficulties by smoothing patches with similar textures, and the same components lacked edge structure information which could be helpful for the classification and failed to present the key evidence for classification. No matter what clustering method (such as k-means and gradient modules) it is combined with, the classification accuracy can reach more than 79%.  
Inspired by SAR images unlike the optical remote sensing images, the paper [47] utilized deep unsupervised discriminative feature learning in order to overcome the lack of labeled samples to train the DNN, which may lead to overfitting.  
For a good deep learning algorithm, besides improving the algorithm's efficiency, it is also very important to improve the diversity of training samples, which is crucial in order to train a robust deep learning model. This paper [48] proposes a data augmentation method, which can decrease the constraints of remote sensing image scenes limited by spectral and topological relations, as well as enhance visual diversity and the reliability of scene classification. The accuracy of the augmentation method can reach 99.127% and 99.297%, respectively.