Change detection in remote sensing images plays an important role in observing earth surface. Over the past few years, deep learning has been widely used in image analysis due to its powerful feature extraction capability, which has shown great potential for change detection task. However, current methods still have difficulties in identifying complex changes due to the insufficient exploration of temporal information. In addition, the complex contextual information of high-resolution images further limits the accuracy. To clarify the temporal information for complex changes and acquire relational contexts of high-resolution images, a dual-perspective change contextual network (DPCC-Net) is proposed for change detection in high-resolution remote sensing images. The presented method emphasizes the process of extraction and optimization of change features by bi-temporal feature fusion and contextual modeling. Firstly, a siamese network is used to extract bi-temporal features. Then, a novel dual-perspective fusion (DPF) is proposed, which takes bi-temporal features as reference respectively and obtains two sets of change features from each temporal perspective, thereby increasing the sensitivity to change related information and changes in complex scenes can be better identified. Next, a change context module (CCM) is proposed to incorporate abundant contexts to change features. CCM considers the relation and similarity between each pixel and its contextual pixels, thereby facilitating the integrity of change objects. The quantitative and qualitative results on three change detection datasets indicate that DPCC-Net achieves state-of-the-art performance. The code of DPCC-Net will be released at: https://github.com/SQD1/DPCC-Net.

遥感影像变化检测在地表观测中发挥着重要作用。在过去的几年里，深度学习由于其强大的特征提取能力被广泛应用于图像分析，在变化检测任务中显示出巨大的潜力。然而，由于对时间信息的探索不足，目前的方法仍然难以识别复杂的变化。此外，高分辨率图像的复杂上下文信息进一步限制了准确性。为了阐明复杂变化的时间信息并获取高分辨率图像的相关上下文，提出了一种用于高分辨率遥感图像变化检测的双视角变化上下文网络（DPCC-Net）。所提出的方法强调通过双时间特征融合和上下文建模来提取和优化变化特征的过程。首先，使用孪生网络来提取双时间特征。然后，提出了一种新颖的双视角融合（DPF），分别以双时态特征为参考，从每个时态视角得到两组变化特征，从而提高对变化相关信息的敏感度，可以在复杂场景中进行变化。更好地识别。接下来，提出了一个变化上下文模块（CCM）来结合丰富的上下文来改变特征。CCM 考虑每个像素与其上下文像素之间的关系和相似性，从而促进变化对象的完整性。三个变化检测数据集的定量和定性结果表明 DPCC-Net 实现了最先进的性能。DPCC-Net的代码将发布在：https://github.com/SQD1/DPCC-Net。