Title :
A support vector method for anomaly detection in hyperspectral imagery
Author :
Banerjee, Amit ; Burlina, Philippe ; Diehl, Chris
Author_Institution :
Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD
Abstract :
This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery
Keywords :
geophysical techniques; geophysics computing; multidimensional signal processing; remote sensing; support vector machines; HYDICE imagery; Reed-Xiaoli detector; SVDD-based detector; WAAMD; anomaly detection; decision rule; false-alarm rate detector; hyperspectral digital imagery collection experiment; hyperspectral imagery; kernel method; nonparametric background model; support vector data description; target detection; wide-area airborne mine detection; Detectors; Hyperspectral imaging; Hyperspectral sensors; Image converters; Kernel; Layout; Object detection; Reflectivity; Shape; Testing; Hyperspectral; support vector data description; target detection;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2006.873019