Evaluation of automated algorithms for small target detection and non-natural terrain characterization using remote multi-band imagery

摘要

Experimental remote sensing data from the 8 to 12 um wavelength NASA Thermal Infrared Multispectral Scanner (TIMS) have been a valuable resource for multispectral algorithm proof-of-concept, a prime example being a Constant False Alarm Rate (CFAR) spectral small target detector founded on maximum likelihood theory; CFAR tests on low signal-to-clutter ratio rural Australian TIMS imagery yielded a detection rate of 5 out of 7 (71%) for small extended targets, e.g. buildings ～ 10 meters in extent, at a 10~6 false alarm rate. Separately, techniques such as Independent Component Analysis (ICA) have since shown good promise for small target detection as well as terrain feature extraction. In this study, we first provide higher-confidence CFAR performance estimates by incorporating a larger set of imagery including ASTER satellite multi-band imagery and ground truth. Secondly, alongside CFAR we perform ICA, which effectively separates many non-natural features from the highly cluttered natural terrain background; in particular, our TIMS results show that a surprisingly small subset of ICA components contain the majority of non-natural "signal" such as paved roads amid the clutter of soil, rock, and vegetation.