Deep Learning for Remote Sensing Image Understanding

These high-level feature representations are more powerful and robust in typical visual tasks. In the intelligent interpretation of remote sensing images, the automatic target detection (or recognition) and high-resolution remotely sensed image classification are two hot topics, and both of these two tasks are carried out by first computing the low-level features in the raw images. For different kinds of remote sensing images (e.g., SAR images and hyperspectral images), the corresponding specific feature representations are available. Through applying deep learning methods, we are free of these handcrafted low-level features and can automatically learn mid-level and higher-level ones from a large amount of unlabeled raw samples beyond the types and domains of remote sensing images. Deep leaning methods can undoubtedly offer better feature representations for the related remote sensing task, and there is a bright prospect of seeing more and more researchers dedicated to learning better features for the target detection and scene classification tasks by utilizing deep learning methods appropriately. This special issue concentrates on the research in new methods, algorithms, and architectures of deep learning to handle the practical challenges in remote sensing image processing. The papers in this issue can be roughly organized into three main categories: (a) remote sensing imagery classification, (b) change detection of multitemporal remote sensing images, and (c) fusion of diverse types of images.