一种改进的星上高光谱异常检测算法研究
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摘要
为解决传统RX异常检测算法导致的星上高光谱图像异常检测准确率低、成本高等问题,对一种改进的星上高光谱异常检测算法进行了研究。在传统RX异常检测算法的基础上,用噪声自适应主成分变换对原始图像进行变换,选择变换后大于设定的合适信噪比阈值的数据;用局部正交子空间投影(LOSP)算法将相应数据投影到正交子空间获得图像残差数据,通过抑制背景等强干扰信息而突出小概率的异常目标信息;用空域滤波方法提取残差数据的特征,将大部分图像信息以某种标准集中于少数的主成分;对获得的波段子集用中值滤波器滤波,消除噪声干扰;用RX异常检测算子对滤波后的波段子集进行异常目标检测。试验结果证明了算法的有效性,算法通过数据量压缩,降低维度、抑制干扰信息,减少了异常检测处理的数据量以提高异常检测效率和精度。
To solve the problems of the low accuracy and high expense of hyperspectral anomaly detection onborne using traditional RX anomaly detection algorithm,an improved algorithm of hyperspectral anomaly detection was studied in this paper.Based on traditional RX anomaly detection algorithm,the original image was transferred by noise adaptive principal component transform.The data which single-to-noise ratio was bigger than threshold set would be selected.The selected data space was projected to orthogonal subspace to obtain the characteristics of the residual error of the image by local orthogonal subspace projection algorithm,so the anomaly target information with low probability could the outstand by suppressing the background and other strong interference.The characteristics of the residual error data were picked up by space-domain filtering method which could make majority information of the original image concentrate on few wave bands under certain standard.The subspace obtained was filtered to eliminate the noise by mean value filter.The anomaly detection of the filtered subspace was carried on using RX anomaly detection algorithm.An experiment showed the effectiveness of the method proposed.The method proposed can reduce the information in order to improve the accuracy and efficiency of anomaly detection onborne by data compressing,dimension reducing and disturbance suppressing.
To solve the problems of the low accuracy and high expense of hyperspectral anomaly detection onborne using traditional RX anomaly detection algorithm,an improved algorithm of hyperspectral anomaly detection was studied in this paper.Based on traditional RX anomaly detection algorithm,the original image was transferred by noise adaptive principal component transform.The data which single-to-noise ratio was bigger than threshold set would be selected.The selected data space was projected to orthogonal subspace to obtain the characteristics of the residual error of the image by local orthogonal subspace projection algorithm,so the anomaly target information with low probability could the outstand by suppressing the background and other strong interference.The characteristics of the residual error data were picked up by space-domain filtering method which could make majority information of the original image concentrate on few wave bands under certain standard.The subspace obtained was filtered to eliminate the noise by mean value filter.The anomaly detection of the filtered subspace was carried on using RX anomaly detection algorithm.An experiment showed the effectiveness of the method proposed.The method proposed can reduce the information in order to improve the accuracy and efficiency of anomaly detection onborne by data compressing,dimension reducing and disturbance suppressing.
引文
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[2]康倩,于晋,林军.星载高光谱成像仪数据地面预处理系统设计[J].航天器工程,2011,20(2):97-101.
[3]濮建福,裴加军,张宁.基于CCSDS的高光谱压缩空谱联合FPGA设计与实现[J].上海航天,2015,32(6):53-57.
[4]相里斌,王忠厚,刘学斌.环境减灾-1A卫星空间调制型干涉光谱成像仪技术[J].航天器工程,2009,18(6):43-49.
[5]袁崇谦,周建勋.卫星遥感技术在油气勘探中的应用[J].海相油气地质,2010,15(2):69-75.
[6]穆欣侃,陈永红,罗海波.星载高光谱图像实时处理系统[J].火力与指挥控制,2016,41(1):22-27.
[7]王彩玲,胡柄樑,王洪伟.约束最大相关系数的高光谱影像目标探测研究[J].激光与红外,2016,46(1):98-102.
[8]林颖,徐卫明,袁立银,等.热红外高光谱系统信号成分分析及处理[J].激光与红外,2010,40(12):1324-1329.
[9]廖佳俊,刘志刚,姜江军.基于稀疏表示分步重构算法的高光谱目标检测[J].红外技术,2016,38(8):699-704.
[10]REED I S,YU X.Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J].IEEE Transactions on Acoustic Speech and Signal Process,1990,38(10):1760-1770.
[11]ROGER R E.A fast way to compute the noise-adjusted principal components transform matrix[J].IEEE Transactions on Geoscience and Remote Sensing,1994,32(11):1194-1196.
[12]CHANG C I,DU Q.Interference and noise-adjusted principal components analysis[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2387-2396.
[13]CHANG C I,DU Q.A joint band prioritization and band decorrelation approach to band selection[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(6):2631-2641.
[14]SD W J,BS G,HL E.Anomaly detection from hyperspectral imagery[J].IEEE Signal Processing Magazine,2002,19(1):58-69.
[15]李智勇.高光谱图像异常检测方法研究[D].长沙:国防科学技术大学,2004.