RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning

TL;DR

RFL-CDNet introduces a richer feature learning framework with deep supervision and novel modules to significantly improve change detection accuracy in remote sensing images.

Contribution

The paper proposes RFL-CDNet, a new change detection framework that enhances intermediate feature utilization through deep supervision, C2FG, and LF modules for superior performance.

Findings

Achieves state-of-the-art results on WHU cultivated land and CDD datasets.

Outperforms existing methods on multiple benchmark datasets.

Demonstrates the effectiveness of richer feature learning in change detection.

Abstract

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub-optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from the previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state-of-the-art performance on WHU cultivated land dataset and CDD dataset, and the second-best performance on WHU building dataset. The source code and models are publicly available at https://github.com/Hhaizee/RFL-CDNet.