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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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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.
hyperspectral target detection;matched filter;background characterization;target leakage;
 Cited by
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