<title>Analysis of hyperspectral infrared and low-frequency SAR data for target classification</title>

Abstract

Multispectral and hyperspectral infrared (IR) sensors have been utilized in the detection of ground targets by exploiting differences in the statistical distribution of the spectral radiance between natural clutter and targets. Target classification by hyperspectral sensors such as the Spatially Modulated Imaging Fourier Transform SPectrometer (SMIFTS) sensor, a mid-wave infrared imager, depends on exploiting target phenomenology in the infrared. Determination of robust components from hyperspectral IR sensors that are useful for discriminating targets is a key issue in classification of ground targets. Both synthetic aperture radars (SAR) and IR imagers have been utilized in the target detection and recognition processes. Improved target classification by sensor fusion depends on exploitation of target phenomenology from both of these sensors. Here we show the results of an investigation of the use of hyperspectral infrared and low-frequency SAR signatures for the purpose of target recognition. Features extracted from both sensors on similar targets are examined in terms of their usefulness in separating between various classes of targets. Simple distance measures are computed to determine the potential for classifying targets based on a fusion of SAR and hyperspectral infrared data. These separability measures are applied to measurements on similar vehicle targets obtained from separate experiments involving the SMIFTS hyperspectral imager and the Stanford Research Institute SAR.