Zhang Jinming, Zang Wenqian, Tian Xiaomin
School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2025 Sep 7;25(17):5581. doi: 10.3390/s25175581.
Multi-temporal remote sensing image matching plays a crucial role in tasks such as detecting changes in urban buildings, monitoring agriculture, and assessing ecological dynamics. Due to temporal variations in images, significant changes in land features can lead to low accuracy or even failure when matching results. To address these challenges, in this study, a remote sensing image matching framework is proposed based on multi-perception and enhanced feature description. Specifically, the framework consists of two core components: a feature extraction network that integrates multiple perceptions and a feature descriptor enhancement module. The designed feature extraction network effectively focuses on key regions while leveraging depthwise separable convolutions to capture local features at different scales, thereby improving the detection capabilities of feature points. Furthermore, the feature descriptor enhancement module optimizes feature point descriptors through self-enhancement and cross-enhancement phases. The enhanced descriptors not only extract the geometric information of the feature points but also integrate global contextual information. Experimental results demonstrate that, compared to existing remote sensing image matching methods, our approach maintains a strong matching performance under conditions of angular and scale variation.
多时相遥感图像匹配在诸如检测城市建筑变化、监测农业以及评估生态动态等任务中起着至关重要的作用。由于图像的时间变化，土地特征的显著变化会导致匹配结果的准确性较低甚至失败。为应对这些挑战，在本研究中，提出了一种基于多感知和增强特征描述的遥感图像匹配框架。具体而言，该框架由两个核心组件组成：一个集成多种感知的特征提取网络和一个特征描述符增强模块。所设计的特征提取网络在利用深度可分离卷积捕捉不同尺度的局部特征时有效地聚焦于关键区域，从而提高特征点的检测能力。此外，特征描述符增强模块通过自我增强和交叉增强阶段优化特征点描述符。增强后的描述符不仅提取特征点的几何信息，还整合全局上下文信息。实验结果表明，与现有的遥感图像匹配方法相比，我们的方法在角度和尺度变化的条件下保持了较强的匹配性能。
