Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images

As an important yet challenging task in Earth observation, change detection (CD) is undergoing a technological revolution, given the broadening application of deep learning. Nevertheless, existing deep learning-based CD methods still suffer from two salient issues: 1) incomplete temporal modeling, and 2) space-time coupling. In view of these issues, we propose a more explicit and sophisticated modeling of time and accordingly establish a pair-to-video change detection (P2V-CD) framework. First, a pseudo transition video that carries rich temporal information is constructed from the input image pair, interpreting CD as a problem of video understanding. Then, two decoupled encoders are utilized to spatially and temporally recognize the type of transition, and the encoders are laterally connected for mutual promotion. Furthermore, the deep supervision technique is applied to accelerate the model training. We illustrate experimentally that the P2V-CD method compares favorably to other state-of-the-art CD approaches in terms of both the visual effect and the evaluation metrics, with a moderate model size and relatively lower computational overhead. Extensive feature map visualization experiments demonstrate how our method works beyond making contrasts between bi-temporal images. Source code is available at https://github.com/Bobholamovic/CDLab.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Change Detection CDD Dataset (season-varying) P2V-CD F1-Score 98.42 # 1
Change detection for remote sensing images CDD Dataset (season-varying) P2V-CD F1-Score 0.9842 # 1
Building change detection for remote sensing images LEVIR-CD P2V-CD F1 91.94 # 9
Change Detection LEVIR-CD P2V-CD F1 91.94 # 9
Change detection for remote sensing images WHU building P2V-CD F1-Score 0.9238 # 1

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