计算机工程

• 图形图像处理 • 上一篇    下一篇

基于自适应回溯联合正交匹配追踪的高光谱图像解混

林雅文,孔繁锵,沈秋,郭文骏   

  1. (南京航空航天大学航天学院,南京 210016)
  • 收稿日期:2014-12-03 出版日期:2015-12-15 发布日期:2015-12-15
  • 作者简介:林雅文(1990-),女,硕士研究生,主研方向:高光谱图像混合像元分解;孔繁锵、沈秋,讲师、博士;郭文骏,硕士研究生。
  • 基金项目:
    国家自然科学基金青年基金资助项目(61401200, 61201365, 61102069);南京航空航天大学青年科技创新基金资助项目(NS2013085)。

Hyperspectral Image Unmixing Based on Adaptive Backtracked Simultaneous Orthogonal Matching Pursuit

LIN Yawen,KONG Fanqiang,SHEN Qiu,GUO Wenjun   

  1. (College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
  • Received:2014-12-03 Online:2015-12-15 Published:2015-12-15

摘要: 为提高高光谱图像的解混精度,针对联合匹配追踪(SMP)和联合正交匹配追踪(SOMP)算法在端元选择机制中存在的非最优问题,提出一种自适应回溯联合正交匹配追踪算法。对高光谱图像进行分块处理,通过初步测试选择每个分块中合适的端元加入端元支撑集,利用终极测试 对支撑集中的端元进行检验,删除其中的错误端元,选取分块端元支撑集的并集作为整幅图像的端元支撑集,并以此为依据进行最小二乘法丰度估计。实验结果表明,与传统凸优化的稀疏解混算法及SMP,SOMP等贪婪算法相比,该算法具有更高的解混精度。

关键词: 高光谱图像, 稀疏解混, 自适应, 回溯, 联合贪婪算法

Abstract: In order to increase the accuracy of hyperspectral unmixing,an Adaptive Backtracked Simultaneous Orthogonal Matching Pursuit(ABSOMP) algorithm is proposed to solve the problem that endmembers selection criterion of Simultaneous Matching Pursuit(SMP) and Simultaneous Orthogonal Matching Pursuit(SOMP) is not optimal in the sense of minimizing the residual of the new approximation.It uses block-processing strategy to divide the whole hyperspectral image into several blocks.Some potential endmembers are selected and added to the estimated endmember set in each block,then ABSOMP incorporates a backtracking process to detect the previous chosen endmembers’ reliability and deletes the unreliable endmembers from the estimated endmember set in each iteration.The endmembers picked in each block are associated as the endmember sets of the whole hyperspectral data.Finally,abundances are estimated using the whole hyperspectral data with obtained endmember sets.Experimental results show that the unmixing accuracy of ABSOMP algorithm is better than traditional convex optimization sparse unmixing algorithm and other greedy algorithms such as SMP and SOMP algorithm.

Key words: hyperspectral image, sparse unmixing, adaptive, backtracked, simultaneous greedy algorithm

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