计算机科学 ›› 2010, Vol. 37 ›› Issue (6): 289-292.

• 图形图像 • 上一篇    下一篇

一种基于随机场模型的高光谱影像目标探测算法

杜博,张良培,李平湘,钟燕飞,陈涛   

  1. (农业部资源遥感与数字农业重点开放实验室 北京100081);(武汉大学计算机学院 武汉430079);(武汉大学测绘遥感信息工程国家重点实验室 武汉430079);(中国地质大学地球物理与空间信息学报 武汉430074)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家“973”计划资助项目(2009CB723905),国家863计划资助项目(2009.AA12G114,2007AA12Z148) ,武汉大学博上研究生科研自主基金(20086190201000061),农业部资源遥感与数字农业重点开放实验室开放基金(RDA080}) ,江西省数字国土重点实验室基金(DLLJ200907),虚拟地理环境教育部重点实验室开放课题,教育部博十点基金新教师项目(200804861058)资助。

Anomaly Detection Method Based on Random Field for Hyperspectral Imagery

DU Bo,ZHANG Liang-pei,LI Ping-xiang,ZHONG Yan-fei,CHEN Tao   

  • Online:2018-12-01 Published:2018-12-01

摘要: 利用随机场模型来描述像元的部域相关性信息,利用这种相关性缩小待探测区域,然后将这种部域信息引入到局域异常探测器中,提出了一种利用随机场模型引入能量函数和部域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法将光谱信息与空间信息相结合,不但比传统算法的探测率更高,且可以更有效地探测出较大的异常目标,探测速度更快。

关键词: 随机场模型,异常探测,能量函数

Abstract: This paper presented an anomaly detection method based on random field model in order to introduce the spatial information between the neighborhood pixels in the hyperspectral imagery into the anomaly detection procedure and reduce the area for detection. In our method, the pixels' neighborhood relationship in the hyperspectral imagery was described by the Random Field model. I}hen this neighborhood relationship information between pixels was introduced into the local-region anomaly detector which uses a nested dual window to detect probable anomaly pixels. Experiments show that this method performs better than the traditional RX-algorithm, especially for the larger anomaly targets which usually contains several neighborhood pixels and with better efficiency.

Key words: Random field, Anomaly detection, Hyperspectral images

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