Remote Sensing Image Change Detection Based on Fully Convolutional Neural Networks with Edge Change Information 基于融合边缘变化信息全卷积神经网络的遥感图像变化检测

Change detection in high resolution remote sensing images is the key to understanding of land surface changes. Change detection of remote sensing images is an important branch of remote sensing image processing. Many existing change detection methods based on deep learning have achieved good results, but it is not easy to obtain the structural details of high resolution remote sensing images, and the accuracy of the detection needs to be improved. Therefore, a network framework which combines Edge change information and channel Attention Network module (EANet) is proposed. EANet is divided into three modules: Edge structure change information detection, depth feature extraction and change area discrimination. Firstly, in order to get the edge change information of the two-phase images, the edge of the two-phase images is detected to get the edge images, and the edge images is subtracted to get the edge difference images. Secondly, in consideration of the fine image details and complex texture features of high resolution remote sensing images, in order to extract fully the depth features of a single image, a model with three branches based on VGG-16 network is constructed to extract the depth features of bitemporal images and edge difference images respectively. Finally, in order to improve the accuracy of the detection, the channel attention mechanism is embedded into the model to focus on the channel features with large amount of information to identify better the changed regions. The experimental results show that the proposed algorithm is superior to some existing methods in terms of visual interpretation and accuracy measurement.