  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.
