Fusion of hyperspectral and ladar data for autonomous target detection

摘要

Robust fusion of data from disparate sensor modalities can provide improved target detection performance over those attainable with the individual sensors. In particular, detection of low-radiance manmade objects or objects under shadow obscuration in hyperspectral imagery (HSI) with acceptable false alarm rates has proven especially challenging. We have developed a fusion algorithm for the enhanced detection of difficult targets when the HSI data is simultaneously collected with LADAR data. Initial detections are obtained by applying a sub-space RX (SSRX) algorithm to the HSI data. In parallel, LADAR-derived digital elevation map (DEM) is segmented and coordinates of objects within a specific elevation range and size are returned to the HSI processor for their spectral signature extraction. Each extracted signature that has not been already detected by SSRX is used in secondary HSI detection employing the adaptive cosine estimator (ACE) algorithm. We show that spatial distribution of ACE score allows for confident discrimination between background elevations and manmade objects. Key to cross-characterization of the data is the accurate co-alignment of the image data. We have also developed an algorithm for automatic co-registration of ladar and HSI imagery, based on the maximization of mutual information, which can provide accurate, sub-pixel registration even if the case when the imaging geometries for the two sensors differ. Details of both algorithms will be presented and results from application to field data will be discussed