Assessment of Noise Reduction of Hyperspectral Imagery using a Target Detection Application

This paper is an evaluation of a previously proposed noise reduction technology for hyperspectral imagery to examine whether it could help to better serve remote sensing applications after noise reduction using the technology. Target detection from hyperspectral imagery is selected as an example for the evaluation. A hyperspectral datacube acquired using the airborne Short-wave-infrared Full Spectrum Image II (SFSI-II) with man-made targets deployed in the scene of the datacube was tested. In addition to an evaluation using receiver operating characteristic (ROC) curve approach, this paper uses a spectral un-mixing technique to generate the fraction images of the target materials, then measures the area of the targets derived from the datacube before and after applying the noise reduction technology and compares the derived target areas to the real targets to assess the detectability of the targets. The area ratio between a derived target and the real target is used as the criterion in the evaluation. The evaluation results showed that the noise reduction technique can help to better serve remote sensing applications. The small targets that cannot be detected from the original datacube were detected after the noise reduction using the technology.