Target detection with compressive sensing hyperspectral images

Yaniv Oiknine; Daniel Gedalin; Isaac August; Dan G. Blumberg; Stanley R. Rotman; Adrian Stern

doi:10.1117/12.2277186

4 October 2017 Target detection with compressive sensing hyperspectral images

Yaniv Oiknine, Daniel Gedalin, Isaac August, Dan G. Blumberg, Stanley R. Rotman, Adrian Stern

Proceedings Volume 10427, Image and Signal Processing for Remote Sensing XXIII; 104270O (2017) https://doi.org/10.1117/12.2277186
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

Abstract

During the past years, several compressive spectral imaging techniques were developed. With these techniques, an optically compressed version of the spectral datacube is captured. Consequently, the information about the object and targets is captured in a lower dimensional space. A question that rises is whether the reduction of the captured space affects the target detection performance. The answer to this question depends on the compressive spectral imaging technique employed. In most compressive spectral imaging techniques, the target detection performance is deteriorated. We show that our recently introduced technique, dubbed Compressive Sensing Miniature Ultra-Spectral Imaging (CSMUSI), yields similar target detection and false detection rates to that of conventional hyperspectral cameras.

Citation Download Citation

Yaniv Oiknine, Daniel Gedalin, Isaac August, Dan G. Blumberg, Stanley R. Rotman, and Adrian Stern "Target detection with compressive sensing hyperspectral images", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270O (4 October 2017); https://doi.org/10.1117/12.2277186

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