基于典型相关分析的遥感影像非监督超像素级变化检测

Unsupervised super pixel level change detection based on canonical correlation analysis

变化检测是指利用多时相影像检测地表覆盖类型发生变化的区域,目前的检测方法易受噪声以及特殊地物等影响,检测结果斑点现象严重、检测精度低。针对以上问题,本文结合典型相关分析和直方图规定化提出一种非监督的超像素级变化检测方法。首先,对两个时刻的遥感影像进行预处理以及超像素分割;其次,基于超像素尺度和未发生变化的概率计算每个超像素的权重;然后,基于超像素级多元变化检测和直方图规定化获取变化特征;最后,基于权重影像、经典方法与变化特征进行决策融合,得到变化检测结果图。本文在3个高光谱测试数据集和一个多光谱测试数据集上进行实验验证。结果表明,本文方法在4个测试数据集上的OA和Kappa指标均为最优,且OA都达到了90%以上。在4个数据集上,本文方法的OA相比于其他方法中的最高精度提高了4.41%、3.44%、1.74%和0.19%。

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 pre‐processed 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 da‐tasets 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 datas‐ets,highlighting its efficacy in enhancing change detection accuracy and reliability.In conclusion,the proposed unsupervised superpixel-lev‐el 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.