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Study on Improving Hyperspectral Target Detection by Target Signal Exclusion in Matched Filtering
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  • Journal title : Korean Journal of Remote Sensing
  • Volume 31, Issue 5,  2015, pp.433-440
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2015.31.5.7
 Title & Authors
Study on Improving Hyperspectral Target Detection by Target Signal Exclusion in Matched Filtering
Kim, Kwang-Eun;
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 Abstract
In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.
 Keywords
hyperspectral target detection;matched filter;background characterization;target leakage;
 Language
Korean
 Cited by
1.
The Investigation of Mineral Distribution at Spirit Rover Landing Site: Gusev Crater by CRISM Hyperspectral data and Target Detection Algorithm, Korean Journal of Remote Sensing, 2016, 32, 5, 403  crossref(new windwow)
2.
An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles, Journal of Sensors, 2017, 2017, 1687-7268, 1  crossref(new windwow)
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