International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
    
    
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
Robust Metric based Anomaly Detection in Kernel Feature Space 
Bo Du'*, Liangpei Zhang”, Huang Xin? 
1 School of Computer Science, Wuhan University 
2 The State Key Laboratory of Information Engineering in 
Surveying, Mapping, and Remote Sensing 
Wuhan University, P.R. China. 
Abstract: This thesis analyzes the anomalous measurement metric in high dimension feature 
space, where it is supposed the Gaussian assumption for state-of-art mahanlanobis algorithms is 
reasonable. The realization of the detector in high dimension feature space is by kernel trick. 
Besides, the masking and swamping effect is further inhibited by an iterative approach in the 
feature space. The proposed robust metric based anomaly detection presents promising 
performance in hyperspectral remote sensing images: the separability between anomalies and 
background is enlarged; background statistics is more concentrated, and immune to the 
contamination by anomalies. 
Keywords: anomaly detection, hyperspectral images, Manhanlobis distance 
Introduction 
Anomaly targets in hyperspectal images (HSI) refer to those deviating obviously from the other 
background pixels, especially by means of the spectral feature [1]. Typical ones are the man-made 
objects in nature scene, such as the vehicles in a grass field. State-of-arts methods mainly evaluate 
it by exploiting the distance of an observing pixel to the background statistics center. So the key is 
the background statistics, or the anomalous metric. RX and its variants take use of a Manhanlobis 
distance from background statistics [2]. In spite of their effectiveness, they are proved to be 
susceptible to the masking and swamping effect, due to the contaminated background statistics [3]. 
Multivariate outlier detection methods, focusing to alleviate this effect, figure out a more robust 
metric by eliminating the probable background pixels or a contracting iteration procedure to 
obtain a new covariance matrix [3, 4]. Traditional ways include iterative exclusion algorithm, with