Leveraging Fine-Grained Information and Noise Decoupling for Remote
  Sensing Change Detection
Qiangang Du Jinlong Peng Changan Wang Xu Chen Qingdong He Wenbing Zhu Mingmin Chi Yabiao Wang Chengjie Wang
Change detection aims to identify remote sense object changes by analyzing
data between bitemporal image pairs. Due to the large temporal and spatial span
of data collection in change detection image pairs, there are often a
significant amount of task-specific and task-agnostic noise. Previous effort
has focused excessively on denoising, with this goes a great deal of loss of
fine-grained information. In this paper, we revisit the importance of
fine-grained features in change detection and propose a series of operations
for fine-grained information compensation and noise decoupling (FINO). First,
the context is utilized to compensate for the fine-grained information in the
feature space. Next, a shape-aware and a brightness-aware module are designed
to improve the capacity for representation learning. The shape-aware module
guides the backbone for more precise shape estimation, guiding the backbone
network in extracting object shape features. The brightness-aware module learns
a overall brightness estimation to improve the model's robustness to
task-agnostic noise. Finally, a task-specific noise decoupling structure is
designed as a way to improve the model's ability to separate noise interference
from feature similarity. With these training schemes, our proposed method
achieves new state-of-the-art (SOTA) results in multiple change detection
benchmarks. The code will be made available.