Toward distortion-aware change detection in realistic scenarios
This addresses a practical issue in remote sensing for applications like environmental monitoring, though it is incremental as it builds on existing CD pipelines.
The paper tackles the problem of geometric distortion in change detection (CD) caused by misaligned remote sensing data from different periods or sensors, proposing a self-supervised framework that improves alignment and enhances CD performance, as demonstrated in two large-scale realistic scenarios.
In the conventional change detection (CD) pipeline, two manually registered and labeled remote sensing datasets serve as the input of the model for training and prediction. However, in realistic scenarios, data from different periods or sensors could fail to be aligned as a result of various coordinate systems. Geometric distortion caused by coordinate shifting remains a thorny issue for CD algorithms. In this paper, we propose a reusable self-supervised framework for bitemporal geometric distortion in CD tasks. The whole framework is composed of Pretext Representation Pre-training, Bitemporal Image Alignment, and Down-stream Decoder Fine-Tuning. With only single-stage pre-training, the key components of the framework can be reused for assistance in the bitemporal image alignment, while simultaneously enhancing the performance of the CD decoder. Experimental results in 2 large-scale realistic scenarios demonstrate that our proposed method can alleviate the bitemporal geometric distortion in CD tasks.