ANOMALY DETECTION IN COMPLEX ENVIRONMENTS: EVALUATION OF THE INTER- AND INTRA-METHOD CONSISTENCY

Many anomaly detection methods, depending on various parameters, have been proposed in literature. Given the diversity of available anomaly detectors, from an 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. Index Terms — Anomaly detection, hyperspectral, clustering, segmentatio

Multi-Level Data Fusion for the Detection of Targets Using Multi-Spectral Image Sequences

This paper presents an approach to the long range automatic detection of vehicles, using multi-sensor image sequences. The method was tested on a database of multi-spectral image sequences, acquired under diverse operational conditions. The approach consists of two parts