CAJ | 학술논문

随着光谱成像技术的发展，高光谱异常检测在遥感图像处理中的应用越来越广泛。传统RX异常检测算法忽略影像空间相关性，而且由于没有经过有效数据降维，运算耗费大，对于高光谱数据有效性不高。高光谱影像在空间和光谱上符合高斯‐马尔科夫模型。通过建立马尔科夫参数能够直接计算协方差矩阵的逆矩阵，避免了高光谱海量数据的庞大计算。提出一种基于三维高斯‐马尔科夫随机场模型的改进RX异常检测算法。该方法用高斯‐马尔科夫随机场模型模拟高光谱影像数据，用最大似然近似法估计高斯‐马尔科夫随机场参数，由高斯‐马尔科夫随机场参数直接构造检测算子，并以待检测像元为中心设置局部优化窗口，称为马尔科夫检测窗。取窗口内数据计算均值向量和协方差逆矩阵，得到中心像元的异常度，通过移动窗口进行逐像元检测。应用AVIRIS高光谱数据对传统RX算法、高斯‐马尔科夫模型背景假设异常检测算法和该算法进行了仿真实验对比。结果表明，该算法能够有效提高高光谱异常检测效率，降低虚警率。运行时间较传统RX算法提高了45.2％，体现出更好的计算效率。
수착광보성상기술적발전，고광보이상검측재요감도상처리중적응용월래월엄범。전통RX이상검측산법홀략영상공간상관성，이차유우몰유경과유효수거강유，운산모비대，대우고광보수거유효성불고。고광보영상재공간화광보상부합고사‐마이과부모형。통과건립마이과부삼수능구직접계산협방차구진적역구진，피면료고광보해량수거적방대계산。제출일충기우삼유고사‐마이과부수궤장모형적개진RX이상검측산법。해방법용고사‐마이과부수궤장모형모의고광보영상수거，용최대사연근사법고계고사‐마이과부수궤장삼수，유고사‐마이과부수궤장삼수직접구조검측산자，병이대검측상원위중심설치국부우화창구，칭위마이과부검측창。취창구내수거계산균치향량화협방차역구진，득도중심상원적이상도，통과이동창구진행축상원검측。응용AVIRIS고광보수거대전통RX산법、고사‐마이과부모형배경가설이상검측산법화해산법진행료방진실험대비。결과표명，해산법능구유효제고고광보이상검측효솔，강저허경솔。운행시간교전통RX산법제고료45.2％，체현출경호적계산효솔。
With the development of spectral imaging technology ,hyperspectral anomaly detection is getting more and more wide‐ly used in remote sensing imagery processing .The traditional RX anomaly detection algorithm neglects spatial correlation of ima‐ges .Besides ,it doesnot validly reduce the data dimension ,which costs too much processing time and shows low validity on hy‐perspectral data .The hyperspectral images follow Gauss‐Markov Random Field (GMRF) in space and spectral dimensions .The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss‐Markov parameters ,which avoids the huge calculation of hyperspectral data .This paper proposes an improved RX anomaly detection algorithm based on three‐dimen‐sional GMRF .The hyperspectral imagery data is simulated with GMRF model ,and the GMRF parameters are estimated with the Approximated Maximum Likelihood method .The detection operator is constructed with GMRF estimation parameters .The detecting pixel is considered as the centre in a local optimization window ,which calls GMRF detecting window .The abnormal degree is calculated with mean vector and covariance inverse matrix ,and the mean vector and covariance inverse matrix are calcu‐lated within the window .The image is detected pixel by pixel with the moving of GMRF window .The traditional RX detection algorithm ,the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data .Simulation results show that the proposed anomaly detection method is able to improve the de‐tection efficiency and reduce false alarm rate .We get the operation time statistics of the three algorithms in the same computer environment .The results show that the proposed algorithm improves the operation time by 45.2% ,which shows good compu‐ting efficiency .