High-Resolution Land Cover Change Detection Using Low-Resolution Labels via a Semi-Supervised Deep Learning Approach - 2021 IEEE Data Fusion Contest Track MSD

Abstract

Classification and change detection of high -resolution remote sensing images using low-resolution labels is a challenging issue in remote sensing community. In this paper, we propose a semi-supervised method based on deep learning to generate high-resolution change maps of Maryland using low-resolution NLCD labels in the Multitemporal Semantic change Detection challenge track (Track MSD) of the 2021 IEEE Data Fusion Contest. Firstly, we refined the NLCD labels using five global land cover products. Subsequently, Fully Convolutional Network (FCN) was trained for the classification of NAIP images with the refined NLCD labels and then the network training was continued with the pseudo-labels from the previous classification results and Modified Normalized Difference Water Index (MNDWI) extracted from Landsat-8 images as the new features. Finally, after the decision-level fusion of the two training periods, change detection results were generated from the bi-temporal classification maps with a post-processing to improve the performance. This algorithm achieved an average Intersection-over-Union (IoU) of 0.6657 on the test dataset and won the 2nd place of the contest.