Algorithms for IR imagery based airborne landmine and minefield detection

In this paper we revisit and enhance various algorithms for landmine detection, discrimination and recognition. Single-band and multi-band medium wave infrared (MWIR) image data from the May data collection (part of Lightweight Airborne multispectral Minefield Detection-Interim (LAMD-I) program,) is used for the analysis. In particular discrimination based on gray-scale moments is explored and its effectiveness is evaluated for surface mines under IR imaging using receiver operating characteristics (ROC) curves. The discriminatory power of gray-scale moments is compared with the RX and matched filter based detectors for different terrain (e.g. grass, sand) and different mine types. The performance of single-band broadband) MWIR imagery is compared with multi-band (short-pass and long-pass) MWIR images. Also direct multi-band detection is compared against fusion of multiple single-band responses. Gray-scale moment based target discrimination at potential target locations, identified by RX or matched filter detectors, is shown to be computationally efficient and provides better performance in terms of reduced false alarms for comparable probability of detection. An evolutionary framework for minefield identification, in the presence of inevitable false targets, is also presented. Starting from the locations of individual mine targets and false alarms, the evolutionary algorithm is used to identify the underlying structure of the minefield. Issues in the detection of different minefield layouts are discussed. Preliminary implementation shows the promise of this approach in identification of a wide variety of minefields.