PPCNET: A Combined Patch-Level and Pixel-Level End-to-End Deep Network for High-Resolution Remote Sensing Image Change Detection

Abstract

Extracting change regions from bitemporal images is crucial to urban planning, land, and resources survey. In the literature, many methods obtaining difference between bitemporal remote sensing images have been proposed. However, there are still some problems due to the complexity of change conditions. In order to solve the above-mentioned problems, we propose a novel network called PPCNET, combining patch-level and pixel-level change detection for bitemporal remote sensing images. This network is divided into three branches: the dual structure is used to extract features of bitemporal images, respectively; changed or unchanged image regions are then detected through fully connected layers, and a soft-max layer at patch level. Once a change is detected at patch level, feature encoder and decoder at pixel level are activated to obtain accurate change boundary. Furthermore, a feature pyramid network-based architecture is employed to fuse information in different layers to further improve change detection effectiveness. Experiments on both satellite and aerial remote sensing images have verified that PPCNET network yields higher change detection accuracy with faster detection speed.