基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法

摘要

基于图像中存在的邻域以及非局部相似等图像空间特征和联合稀疏解混思想,该文提出一种基于高光谱图像光谱相似性度量的多任务联合稀疏解混方法.通过高光谱图像的光谱特性统计值设定光谱度量阈值,对高光谱图像中相似的像元光谱进行光谱相似性度量分组,再对分组像元光谱数据进行多任务联合稀疏光谱解混模型的构建和求解,得到最终的丰度系数.模拟数据实验结果表明,该方法一定程度上提升了现有联合稀疏光谱解混方法的丰度估计精度,真实数据结果也验证了方法的有效性.%In this paper, a multi-task jointly sparse spectral unmixing method based on spectral similarity measure of hyperspectral imagery is proposed, which is a refinement of collaborative sparse spectral unmixing method. First, a threshold value is obtained through the statistical characters of some random selected neighboring pixels in hypersepctral image. Second, all pixels of hyperspectral image are grouped by a spectral similarity measure and the threshold value. Then, a multi-task jointly sparse optimization problem is constructed and solved for the grouped pixels, and the abundance coefficients are obtained finally. Experimentals results on synthetic and real hyperspectral image demonstrate the effectiveness of the proposed approach.