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Novel automatic target recognition approach for multispectral data

Automating the detection and identification of significant threats using multispectral (MS) imagery is a critical issue in remote sensing. Unlike previous multispectral target recognition approaches, we utilize a three-stage process that not only takes into account the spectral content, but also the spatial information. The first stage applies a matched filter to the calibrated MS data. Here, the matched filter is tuned to the spectral components of a given target and produces an image intensity map of where the best matches occur. The second stage represents a novel detection algorithm, known as the focus of attention (FOA) stage. The FOA performs an initial screening of the data based on intensity and size checks on the matched filter output. Next, using the target's pure components, the third stage performs constrained linear unmixing on MS pixels within the FOA detected regions. Knowledge sources derived from this process are combined using a sequential probability ratio test (SPRT). The SPRT can fuse contaminated, uncertain and disparate information from multiple sources. We demonstrate our approach on identifying a specific target using actual data collected in ideal conditions and also use approximately 35 square kilometers of urban clutter as false alarm data.