摘要
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.
出处
《中国图象图形学报》
CSCD
北大核心
2011年第11期2081-2087,共7页
Journal of Image and Graphics
基金
国家自然科学基金项目(61071163)
