Unsupervised super pixel level change detection based on canonical correlation analysis

Change detection, a critical task in remote sensing and geospatial analysis, involves the identification of areas where alterations in land cover types have occurred over time using multi-temporal images. The accurate detection of such changes is essential for various applications, including environmental monitoring, urban development assessment, and natural disaster management. However, existing change detection methods are often susceptible to noise and the influence of specific land features, resulting in significant speckle phenomena and reduced detection accuracy. These limitations hinder the reliable identification of change patterns in land cover, impacting the effectiveness of downstream analyses and decision-making processes. To address these challenges, this paper proposes an unsupervised superpixel-level change detection method that combines canonical correlation analysis and histogram matching. This method aims to improve the accuracy and reliability of change detection by addressing the limitations associated with traditional approaches. The proposed method consists of several steps. First, the remote sensing images were preprocessed and superpixel-segmented. This step is aimed at improving the quality of the image and dividing it into homogeneous regions called superpixels. Superpixel segmentation helps to preserve spatial information and reduces the influence of noise on subsequent analysis. Next, the weight of each superpixel was calculated based on the superpixel scale and the unchanged probability. Superpixel weights are used to highlight the importance of different regions in the change detection process. After obtaining the weights, the method proceeds to extract change features at the superpixel level using multivariate change detection and histogram matching. Multivariate change detection involves analyzing the spectral information of the superpixels to identify changes in land cover types. Histogram matching, on the other hand, aims to align the histograms of the superpixels from different time periods, enabling more accurate comparison and detection of changes. Finally, a change detection result map was developed based on the weighted image, classical methods, and change features.Three hyperspectral test datasets and one multispectral test dataset were used for experimental verification. Experimental validation of the proposed method was conducted on three hyperspectral test datasets and one multispectral test dataset. The results demonstrate the superior performance of the proposed method, with the Overall Accuracy (OA) and Kappa index surpassing those of existing methods across all four test datasets. Specifically, the OA values consistently exceed 90% on all datasets, indicating the high accuracy and robustness of the proposed method. Moreover, comparative analysis reveals significant improvements in the OA when compared to other existing methods. The proposed method achieves an OA increase of 4.41%, 3.44%, 1.74%, and 0.19% on the four datasets, highlighting its efficacy in enhancing change detection accuracy and reliability. In conclusion, the proposed unsupervised superpixel-level change detection method, which integrates canonical correlation analysis and histogram matching, demonstrates remarkable performance in detecting changes in land cover types from multi-temporal remote sensing images. © 2024 Science Press. All rights reserved.