摘要：

针对相同地理空间、不同时相的高分辨率遥感图像之间受季节性变化、气候、光照等干扰因素影响的问题, 提出一种基于多时相ChangeFormer的遥感图像建筑物变化检测(CD)方法。该方法使用多个不同时相的遥感图像, 将最新时相遥感图像与变化前的多个遥感图像在特征差异提取上进行不同尺度下的融合, 分别关注图像的综合语义特征以及图像之间语义信息的细节。该方法有助于减少季节、光照等因素发生变化时引起的误检。同时, 考虑变化前多个不同时相的遥感图像, 将其特征差异进行融合并引入正则化项作为损失函数, 进一步消除非建筑物变化以及建筑物非变化区域光照阴影带来的干扰, 提高模型的泛化能力。构建从农业土地耕地到建筑用地变化的三时相遥感图像数据集, 实验结果表明, 相较于目前最优的BIT方法, 多时相ChangeFormer方法在F1值、交并比(IoU)、精确率和召回率指标上分别提升了9.04%、9.87%、15.27%和3.4%, 显著提高了检测精度, 且在细节信息处理方面明显优于经典的CD方法。

关键词: 多时相, 干扰因素, 变化检测, 特征差异, 不同尺度融合, ChangeFormer, 正则化

Abstract:

To address the issue of interference factors such as seasonal changes, climate, and illumination that affect high-resolution remote sensing images of the same geographical space but different temporal phases, a remote sensing image building Change Detection (CD) method based on a multi-temporal ChangeFormer is proposed. This method uses multiple remote sensing images from different temporal phases and fuses the latest temporal remote sensing image with multiple prechange remote sensing images at different scales for feature difference extraction. Additionally, it focuses on both the comprehensive semantic features of the images and the details of the semantic information between the images. This approach helps reduce false detections caused by changes in factors such as season and illumination. Additionally, the method fuses the feature differences of multiple prechange remote sensing images from different temporal phases and introduces a regularization term as a loss function. This eliminates interference from nonbuilding changes and illumination shadows in the nonchanging areas of buildings, thereby enhancing the generalization ability of the model. A three-temporal remote sensing image dataset covering changes from agricultural land to construction land is constructed. The experimental results show that, compared to the current optimal BIT method, the multi-temporal ChangeFormer method improves the F1 value, Intersection over Union (IoU), precision, and recall by 9.04%, 9.87%, 15.27%, and 3.4%, respectively, thus significantly enhancing detection accuracy. Furthermore, it outperforms classical CD methods in terms of detailed information processing.

Key words: multi-temporal, interfering factors, Change Detection (CD), characteristic difference, different scale fusion, ChangeFormer, regularization