Anomaly detection in complex environments: Evaluation of the inter-and intra-method consistency

• Published in: 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing pp. 1 - 4
• Main Authors: Borghys, D., Truyen, E., Shimoni, M., Perneel, C.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-08-2009
• Subjects: Anomaly detection; clustering; Detectors; Gaussian distribution; hyperspectral; Hyperspectral imaging; Hyperspectral sensors; Image segmentation; Kernel; Layout; Mathematics; segmentation; Sensor phenomena and characterization; Testing
• ISBN: 9781424446865; 1424446864
• ISSN: 2158-6268; 2158-6276
• DOI: 10.1109/WHISPERS.2009.5289040

Many anomaly detection methods, depending on various parameters, have been proposed in literature. Given the diversity of available anomaly detectors, froman operational viewpoint it is interesting to determine an efficient strategy to find the best suited detector for a given application. This is not obvious, especially in scenes with a highly structured background. The work presented here proposes a generic approach to the problem by examining the following questions: How different are the results of the various anomaly detectors? Are the parameters influencing the results significantly? Are there classes of methods sufficiently similar so that one can test only one of each class and see which results are most adequate for a given application? What are the spectral/spatial characteristics of the differences between methods? Can one predict which detector will give the best results for a given application? The current paper tries to answer the first three questions by comparing results of different types of anomaly detectors applied to different complex (urban, industrial and harbor) scenes. The comparison is not in absolute terms because it does not rely on a priori ground truth. In stead the detectors are compared relative to one another, the aim being to evaluate the similarities between the performance of the detectors and the dependency of their results on the used parameters, i.e. the inter- and intra method consistency.