Due to differences in external imaging conditions, multispectral images taken at different periods are subject to radiation differences, which severely affect the detection accuracy. To solve this problem, a modified algorithm based on slow feature analysis is proposed for multispectral image change detection. First, single-band slow feature analysis is performed to process bitemporal multispectral images band by band. In this way, the differences between unchanged pixels in each pair of single-band images can be sufficiently suppressed to obtain multiple feature-difference images containing real change information. Then, the feature-difference images of each band are fused into a grayscale distance image using the Euclidean distance. After Gaussian filtering of the grayscale distance image, false detection points can be further reduced. Finally, the k-means clustering method is performed on the filtered grayscale distance image to obtain the binary change map. Experiments reveal that our proposed algorithm is less affected by radiation differences and has obvious advantages in time complexity and detection accuracy.

中文翻译：

---

基于单波段慢特征分析的多光谱图像变化检测

由于外界成像条件的差异，不同时期拍摄的多光谱图像存在辐射差异，严重影响检测精度。针对这一问题，提出了一种基于慢特征分析的改进算法，用于多光谱图像变化检测。首先，执行单波段慢特征分析以逐波段处理双时相多光谱图像。这样可以充分抑制每对单波段图像中不变像素之间的差异，从而获得包含真实变化信息的多幅特征差异图像。然后，使用欧几里德距离将每个波段的特征差异图像融合为灰度距离图像。对灰度距离图像进行高斯滤波后，可以进一步减少误检点。最后，对滤波后的灰度距离图像进行k-means聚类，得到二值变化图。实验表明，我们提出的算法受辐射差异的影响较小，在时间复杂度和检测精度上具有明显的优势。