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- W2394725790 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." @default.
- W2394725790 created "2016-06-24" @default.
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- W2394725790 date "2015-10-30" @default.
- W2394725790 modified "2023-10-17" @default.
- W2394725790 title "Study on Improving Hyperspectral Target Detection by Target Signal Exclusion in Matched Filtering" @default.
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- W2394725790 doi "https://doi.org/10.7780/kjrs.2015.31.5.7" @default.
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