CDXFormer: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory

• URL: http://arxiv.org/abs/2411.07863v1
• Date: Tue, 12 Nov 2024 15:22:14 GMT
• Title: CDXFormer: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory
• Authors: Zhenkai Wu, Xiaowen Ma, Rongrong Lian, Zhentao Lin, Wei Zhang,
• Abstract summary: We propose CDXFormer, with a core component that is a powerful XLSTM-based spatial enhancement layer. We introduce a scale-specific Feature Enhancer layer, incorporating a Cross-Temporal Global Perceptron customized for semantic-accurate deep features. We also propose a Cross-Scale Interactive Fusion module to progressively interact global change representations with responses.
• Score: 3.119836924407993
• License:
• Abstract: In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current RS-CD methods lack a balanced consideration of performance and efficiency. CNNs lack global context, Transformers have quadratic computational complexity, and Mambas are restricted by CUDA acceleration. In this paper, we propose CDXFormer, with a core component that is a powerful XLSTM-based feature enhancement layer, integrating the advantages of linear computational complexity, global context perception, and strong interpret-ability. Specifically, we introduce a scale-specific Feature Enhancer layer, incorporating a Cross-Temporal Global Perceptron customized for semantic-accurate deep features, and a Cross-Temporal Spatial Refiner customized for detail-rich shallow features. Additionally, we propose a Cross-Scale Interactive Fusion module to progressively interact global change representations with spatial responses. Extensive experimental results demonstrate that CDXFormer achieves state-of-the-art performance across three benchmark datasets, offering a compelling balance between efficiency and accuracy. Code is available at https://github.com/xwmaxwma/rschange.

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