摘要
结合空间信息约束的高光谱稀疏解混技术是高光谱图像稀疏解混领域的研究热点之一。为了克服高光谱图像在自然场景中的空间结构难以精确表示的缺点,本文提出了一种多尺度光谱相似性指导的高光谱解混算法。首先,将高光谱图像分割成具有空间结构的近似域光谱图像;然后,根据相邻超像素之间的相似性进行近似域稀疏解混;最后,将近似域解混结果转换到原始域并结合实际像素光谱进行原始域的逐像素精确解混。实验结果表明,本文所提出的算法相比同类算法有更低的解混复杂度,且具有更高的解混精度和鲁棒性。
Sparse Unmixing(SU) combined with spatial information constraints is one of the research hotspots in the field of Hyperspectral Unmixing(HU). In order to overcome the shortcomings that the spatial structure of hyperspectral images in natural scenes is difficult to accurately represent. Hence, a Hyperspectral Unmixing algorithm guided by multiscale spectral similarity was proposed in this paper. First, segment the Hyperspectral Image into an approximate domain spectral image with a spatial structure. Then, the approximate domain SU was performed according to the similarity between adjacent superpixels. Finally, the approximate domain unmixing result was converted to the original domain, and combined with the actual pixel spectrum to perform accurate pixel-by-pixel unmixing of the original domain. Experimental results show that the algorithm proposed has lower unmixing complexity than similar algorithms, and has higher unmixing accuracy and robustness.
作者
王艳红
崔子冠
干宗良
唐贵进
刘峰
WANG Yanhong;CUI Ziguan;GAN Zongliang;TANG Guijin;LIU Feng(Image Processing and Image Communication Lab,Nanjing University of Posts and Telecommunications,Nanjing,Jiangsu 210003,China)
出处
《信号处理》
CSCD
北大核心
2021年第3期417-427,共11页
Journal of Signal Processing
基金
国家自然科学基金(61501260)
南京邮电大学“1311人才计划”项目。
关键词
高光谱图像解混
超像素
空间约束
稀疏解混
hyperspectral unmixing
superpixels
spatial constraints
sparse unmixing
