Change Cross-Detection Based on Label Improvements and Multi-Model Fusion for Multi-Temporal Remote Sensing Images
Zhuohong Li, Fangxiao Lu, Hongyan Zhang, Guangyi Yang, Liangpei Zhang
Abstract
Change detection is a geospatial application for social good whose development is restricted by a slow-growing labeling technology and outdated classification labels for remotely sensed images. In this paper, a change cross-detection method based on label improvements and multi-model fusion is proposed for Multi-temporal Semantic change Detection (MSD) with weak, noisy, and low-resolution labels. For unmatched labels, a Siamese Skip_FCN network is proposed to generate preliminary labels at high-resolution. Subsequently, a multi-model fusion method is introduced to perform accurate and stable land cover classification. In addition, a cross-detection structure is used to generate high precision change maps and a post-processing step further improves the final results. In the track MSD of the 2021 Data Fusion Contest (DFC21-MSD), the proposed method achieved a mean intersection over union (mIoU) of 70.25% in phase 1 and 67.72% in phase 2, ranking first in both phases [1].