Continuous Cross-resolution Remote Sensing Image Change Detection
- URL: http://arxiv.org/abs/2305.14722v2
- Date: Sat, 21 Oct 2023 09:46:26 GMT
- Title: Continuous Cross-resolution Remote Sensing Image Change Detection
- Authors: Hao Chen, Haotian Zhang, Keyan Chen, Chenyao Zhou, Song Chen, Zhengxia
Zou, Zhenwei Shi
- Abstract summary: Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions.
We propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences.
Our method significantly outperforms several vanilla CD methods and two cross-resolution CD methods on three datasets.
- Score: 28.466756872079472
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most contemporary supervised Remote Sensing (RS) image Change Detection (CD)
approaches are customized for equal-resolution bitemporal images. Real-world
applications raise the need for cross-resolution change detection, aka, CD
based on bitemporal images with different spatial resolutions. Given training
samples of a fixed bitemporal resolution difference (ratio) between the
high-resolution (HR) image and the low-resolution (LR) one, current
cross-resolution methods may fit a certain ratio but lack adaptation to other
resolution differences. Toward continuous cross-resolution CD, we propose
scale-invariant learning to enforce the model consistently predicting HR
results given synthesized samples of varying resolution differences.
Concretely, we synthesize blurred versions of the HR image by random
downsampled reconstructions to reduce the gap between HR and LR images. We
introduce coordinate-based representations to decode per-pixel predictions by
feeding the coordinate query and corresponding multi-level embedding features
into an MLP that implicitly learns the shape of land cover changes, therefore
benefiting recognizing blurred objects in the LR image. Moreover, considering
that spatial resolution mainly affects the local textures, we apply
local-window self-attention to align bitemporal features during the early
stages of the encoder. Extensive experiments on two synthesized and one
real-world different-resolution CD datasets verify the effectiveness of the
proposed method. Our method significantly outperforms several vanilla CD
methods and two cross-resolution CD methods on the three datasets both in
in-distribution and out-of-distribution settings. The empirical results suggest
that our method could yield relatively consistent HR change predictions
regardless of varying bitemporal resolution ratios. Our code is available at
\url{https://github.com/justchenhao/SILI_CD}.
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