Domain Adaptive Cross Reconstruction for Change Detection of Heterogeneous Remote Sensing Images via a Feedback Guidance Mechanism
Qiang Liu, Jiancheng Li, Xiangchao Meng, Feng Shao
Abstract
Change detection on heterogeneous optical and synthetic aperture radar (SAR) images is soaring and plays a crucial role in monitoring land cover changes, such as disaster emergencies and natural resource monitoring. This is commonly recognized as a promising but challenging work due to the intrinsic differences in imaging mechanisms between the optical and SAR images. Recently, deep learning-based change detection methods based on two-step processing have attracted attention, i.e., first image translation between optical and SAR images to alleviate their modality differences and then change detection based on the translated images. However, image translation itself is a trouble task for the heterogeneous optical and SAR images. The unreliable image translation results further limit the accuracy of change detection. In this paper, to mitigate this problem, we propose a change detection model on domain adaptation by novelty integrating change detection and image reconstruction into a unified framework. Specifically, we first transform the optical and SAR images into an intermediate common domain for comparison. Moreover, cross reconstruction for optical and SAR images is designed to maintain the characteristics of the images and improve the performance of domain adaptation. In addition, a feedback guidance mechanism is circumspectly designed to co-optimize change detection and image reconstruction tasks. Extensive experiments were conducted on four publicly available datasets, the results demonstrate the effectiveness of our proposed method.