Heterogeneous Remote Sensing Images Change Detection (HRSICD) is a significant challenge in remote sensing image processing, with substantial application value in rapid natural disaster response. However, significant differences in imaging modalities often result in poor comparability of their features, affecting the recognition accuracy. To address the issue, we propose a novel HRSICD method based on image structure relationships and semantic information. First, we employ a Multi-scale Pyramid Convolution Encoder to efficiently extract the multi-scale and detailed features. Next, the Cross-domain Feature Alignment Module aligns the structural relationships and semantic features of the heterogeneous images, enhancing the comparability between heterogeneous image features. Finally, the Multi-level Decoder fuses the structural and semantic features, achieving refined identification of change areas. We validated the advancement of proposed method on five publicly available HRSICD datasets. Additionally, zero-shot generalization experiments and real-world applications were conducted to assess its generalization capability. Our method achieved favorable results in all experiments, demonstrating its effectiveness. The code of the proposed method will be made available at https://github.com/Lucky-DW/HRSICD.

中文翻译：

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面向灾害应急响应的异构低分辨率遥感影像精细化变化检测


异构遥感影像变化检测 （HRSICD） 是遥感影像处理中的一大挑战，在自然灾害快速响应方面具有重要的应用价值。然而，成像模式的显著差异往往导致其特征的可比性差，从而影响识别准确性。为了解决这个问题，我们提出了一种基于图像结构关系和语义信息的新型 HRSICD 方法。首先，我们采用多尺度金字塔卷积编码器来有效地提取多尺度和细节特征。接下来，跨域特征对齐模块对齐异构图像的结构关系和语义特征，增强异构图像特征之间的可比性。最后，Multi-level Decoder 融合了结构和语义特征，实现了对变化区域的精细识别。我们在五个公开可用的 HRSICD 数据集上验证了所提出的方法的进展。此外，还进行了零镜头泛化实验和实际应用，以评估其泛化能力。我们的方法在所有实验中都取得了良好的结果，证明了其有效性。所建议方法的代码将在 https://github.com/Lucky-DW/HRSICD 上提供。