CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement
This work addresses style variability issues in remote sensing change detection, an incremental improvement for applications like environmental monitoring.
The paper tackles the problem of style variations in bi-temporal remote sensing images hindering change detection accuracy by proposing CFNet, which uses a content-aware strategy and a reweighting module to focus on content features, achieving F1 scores of 81.41% on CLCD, 92.18% on LEVIR-CD, and 82.89% on SYSU-CD.
Change detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bi-temporal remote sensing images often exhibit significant differences in image style. Even with the powerful generalization capabilities of DNNs, these unpredictable style variations between bi-temporal images inevitably affect model's ability to accurately detect changed areas. To address issue above, we propose the Content Focuser Network (CFNet), which takes content-aware strategy as a key insight. CFNet employs EfficientNet-B5 as the backbone for feature extraction. To enhance the model's focus on the content features of images while mitigating the misleading effects of style features, we develop a constraint strategy that prioritizes the content features of bi-temporal images, termed Content-Aware. Furthermore, to enable the model to flexibly focus on changed and unchanged areas according to the requirements of different stages, we design a reweighting module based on the cosine distance between bi-temporal image features, termed Focuser. CFNet achieve outstanding performance across three well-known change detection datasets: CLCD (F1: 81.41%, IoU: 68.65%), LEVIR-CD (F1: 92.18%, IoU: 85.49%), and SYSU-CD (F1: 82.89%, IoU: 70.78%). The code and pretrained models of CFNet are publicly released at https://github.com/wifiBlack/CFNet.