传统的基于结构特征的遥感图像变化检测方法,易受成像稳定性的影响而误差很大。针对图像内在的稀疏性结构信息,提出基于压缩感知(CS)的遥感图像变化检测方法。通过自适应构造超完备字典将图像局部信息投影到高维空间中,实现图像的稀疏表示,并运用随机矩阵得到了数据在高维空间中的低维特征子空间。最后利用模糊C均值(FCM)聚类算法进行无监督聚类,实现遥感图像变化区域信息的重构。实验结果表明,本文方法不仅能够很好的检测出图像的轮廓变化和图像的区域变化,而且对噪声具有很好的鲁棒性。

Traditional remote sensing image change detection approaches based on structure features are usually limited by imaging stability. In this paper, we introduce a new method for unsupervised change detection in remote sensing images using compressive sensing (CS) based on the image inherent sparse structures. For this algorithm, a large collection of image patches is projected onto high dimensional spaces through redundant dictionary, giving an adaptive sparse representation per each image patch. A random matrix is taken as measurement matrix to realize feature space dimension reduction. Then, the final change detection is realized by clustering the feature vector space using the fuzzy C-mean clustering(FCM)algorithm, achieving the reconstruction of change regional information. The experimental results demonstrate that the proposed algorithm has good change detection results both in contour and region and has a good robustness.