SACTNet: Spatial Attention Context Transformation Network for Cloud Removal

Optical remote sensing image has the advantages of fast information acquisition, short update cycle, and dynamic monitoring. It plays an important role in many earth observation activities, such as ocean monitoring, meteorological observation, land planning, and crop yield investigation. However, in the process of image acquisition, an optical remote sensing system is often disturbed by clouds, resulting in low image clarity or even loss of ground information, affecting the acquisition of feature information and subsequent applications. We propose a spatial attention recurrent neural network model combined with a context transformation network to overcome the challenge of cloud occlusion. This model can obtain the core information in remote sensing images and consider the remote dependencies in the network. Furthermore, the network proposed in this paper has achieved excellent performance on the RICE1 and RICE2 datasets.

Change Capsule Network for Optical Remote Sensing Image Change Detection

Change detection based on deep learning has made great progress recently, but there are still some challenges, such as the small data size in open-labeled datasets, the different viewpoints in image pairs, and the poor similarity measures in feature pairs. To alleviate these problems, this paper presents a novel change capsule network by taking advantage of a capsule network that can better deal with the different viewpoints and can achieve satisfactory performance with small training data for optical remote sensing image change detection. First, two identical non-shared weight capsule networks are designed to extract the vector-based features of image pairs. Second, the unchanged region reconstruction module is adopted to keep the feature space of the unchanged region more consistent. Third, vector cosine and vector difference are utilized to compare the vector-based features in a capsule network efficiently, which can enlarge the separability between the changed pixels and the unchanged pixels. Finally, a binary change map can be produced by analyzing both the vector cosine and vector difference. From the unchanged region reconstruction module and the vector cosine and vector difference module, the extracted feature pairs in a change capsule network are more comparable and separable. Moreover, to test the effectiveness of the proposed change capsule network in dealing with the different viewpoints in multi-temporal images, we collect a new change detection dataset from a taken-over Al Udeid Air Basee (AUAB) using Google Earth. The results of the experiments carried out on the AUAB dataset show that a change capsule network can better deal with the different viewpoints and can improve the comparability and separability of feature pairs. Furthermore, a comparison of the experimental results carried out on the AUAB dataset and SZTAKI AirChange Benchmark Set demonstrates the effectiveness and superiority of the proposed method.