Abstract: Provided is a process and system for detection of sparse or otherwise weak targets in a hyperspectral image. A hyperspectral image is received having a multitude of pixels, with each pixel having a respective spectrum. In some embodiments, multiple mean spectra are selectively determined for respective sub-regions of the hyperspectral image. The subset mean spectra can be selectively removed from respective pixels, thereby improving image fidelity due to sensor artifacts. Additionally, target detection of such an adjusted image can be determined by one or more of matched filter techniques or by partial un-mixing. In at least some embodiments target detection is enhanced by combining a measure of target match with a residual spectrum determined as a measure of un-match. Target detection can be further improved by application of rules, for example, related to target detection threshold.

Abstract: Provided is a process and system for detection of sparse or otherwise weak targets in a hyperspectral image. A hyperspectral image is received having a multitude of pixels, with each pixel having a respective spectrum. In some embodiments, multiple mean spectra are selectively determined for respective sub-regions of the hyperspectral image. The subset mean spectra can be selectively removed from respective pixels, thereby improving image fidelity due to sensor artifacts. Additionally, target detection of such an adjusted image can be determined by one or more of matched filter techniques or by partial un-mixing. In at least some embodiments target detection is enhanced by combining a measure of target match with a residual spectrum determined as a measure of un-match. Target detection can be further improved by application of rules, for example, related to target detection threshold.

Abstract: Provided is a process and system for detection of sparse or otherwise weak targets in a hyperspectral image. The method includes receiving a hyperspectral image having a plurality of pixels, with each pixel having a respective spectrum. Multiple mean spectra are selectively determined for respective sub-regions of the hyperspectral image. The subset mean spectra are selectively removed from respective pixels, thereby improving image fidelity due to sensor artifacts. Additionally, target detection of such an adjusted image can be determined by one or more of matched filter techniques or by partial un-mixing. In some embodiments target detection is enhanced by combining a measure of target match with a measure of un-match. Target detection can be further improved by application of rules, for example, related to target detection threshold.