Matches in SemOpenAlex for { <https://semopenalex.org/work/W2928758868> ?p ?o ?g. }
- W2928758868 endingPage "462" @default.
- W2928758868 startingPage "435" @default.
- W2928758868 abstract "In this work, a novel target detector for hyperspectral imagery is developed. The detector is independent on the unknown covariance matrix, behaves well in large dimensions, distributional free, invariant to atmospheric effects, and does not require a background dictionary to be constructed. Based on a modification of the robust principal component analysis (RPCA)Robust principal component analysis, a given hyperspectral image (HSI) is regarded as being made up of the sum of a low-rank background HSI and a sparse target HSI that contains the targets based on a pre-learned target dictionaryTarget dictionary specified by the user. The sparse component is directly used for the detection, that is, the targets are simply detected at the non-zero entries of the sparse target HSI. Hence, a novel target detector is developed, which is simply a sparse HSI generated automatically from the original HSI, but containing only the targets with the background is suppressed. The detector is evaluated on real experiments, and the results of which demonstrate its effectiveness for hyperspectral target detectionHyperspectral target detection especially when the targets are well matched to the surroundings." @default.
- W2928758868 created "2019-04-11" @default.
- W2928758868 creator A5022689387 @default.
- W2928758868 creator A5030078173 @default.
- W2928758868 creator A5038218941 @default.
- W2928758868 creator A5060737319 @default.
- W2928758868 date "2020-01-01" @default.
- W2928758868 modified "2023-09-25" @default.
- W2928758868 title "Automatic Target Detection for Sparse Hyperspectral Images" @default.
- W2928758868 cites W1537281681 @default.
- W2928758868 cites W1964549775 @default.
- W2928758868 cites W1965125844 @default.
- W2928758868 cites W1970099214 @default.
- W2928758868 cites W1972578813 @default.
- W2928758868 cites W1974640819 @default.
- W2928758868 cites W1980687383 @default.
- W2928758868 cites W1985133440 @default.
- W2928758868 cites W1986921156 @default.
- W2928758868 cites W1988946738 @default.
- W2928758868 cites W1990953362 @default.
- W2928758868 cites W1996726072 @default.
- W2928758868 cites W1997201895 @default.
- W2928758868 cites W2000016398 @default.
- W2928758868 cites W2012749606 @default.
- W2928758868 cites W2012961725 @default.
- W2928758868 cites W2012973816 @default.
- W2928758868 cites W2013680147 @default.
- W2928758868 cites W2019149505 @default.
- W2928758868 cites W2023360712 @default.
- W2928758868 cites W2029213856 @default.
- W2928758868 cites W2032223996 @default.
- W2928758868 cites W2035128422 @default.
- W2928758868 cites W2037143195 @default.
- W2928758868 cites W2040325979 @default.
- W2928758868 cites W2045983409 @default.
- W2928758868 cites W2058414375 @default.
- W2928758868 cites W2062125287 @default.
- W2928758868 cites W2063978378 @default.
- W2928758868 cites W2067782748 @default.
- W2928758868 cites W2070112929 @default.
- W2928758868 cites W2074682976 @default.
- W2928758868 cites W2075569011 @default.
- W2928758868 cites W2078296814 @default.
- W2928758868 cites W2087263574 @default.
- W2928758868 cites W2091397530 @default.
- W2928758868 cites W2091707925 @default.
- W2928758868 cites W2096972831 @default.
- W2928758868 cites W2103972604 @default.
- W2928758868 cites W2107861471 @default.
- W2928758868 cites W2110211064 @default.
- W2928758868 cites W2111560151 @default.
- W2928758868 cites W2112229432 @default.
- W2928758868 cites W2126607811 @default.
- W2928758868 cites W2127450210 @default.
- W2928758868 cites W2129812935 @default.
- W2928758868 cites W2131697388 @default.
- W2928758868 cites W2135046866 @default.
- W2928758868 cites W2140121437 @default.
- W2928758868 cites W2145962650 @default.
- W2928758868 cites W2150347412 @default.
- W2928758868 cites W2152734820 @default.
- W2928758868 cites W2160675285 @default.
- W2928758868 cites W2163886442 @default.
- W2928758868 cites W2163957348 @default.
- W2928758868 cites W2165916500 @default.
- W2928758868 cites W2166682552 @default.
- W2928758868 cites W2167799103 @default.
- W2928758868 cites W2169978421 @default.
- W2928758868 cites W2194818953 @default.
- W2928758868 cites W2228126342 @default.
- W2928758868 cites W2288752886 @default.
- W2928758868 cites W2295576075 @default.
- W2928758868 cites W2330747182 @default.
- W2928758868 cites W2500876844 @default.
- W2928758868 cites W2518815253 @default.
- W2928758868 cites W2550571249 @default.
- W2928758868 cites W2685630698 @default.
- W2928758868 cites W2765358207 @default.
- W2928758868 cites W2773266593 @default.
- W2928758868 cites W2791928749 @default.
- W2928758868 cites W2886770693 @default.
- W2928758868 cites W2963172671 @default.
- W2928758868 cites W2963938246 @default.
- W2928758868 cites W3100364246 @default.
- W2928758868 cites W3104349486 @default.
- W2928758868 cites W4235838711 @default.
- W2928758868 cites W4240808349 @default.
- W2928758868 cites W4247093658 @default.
- W2928758868 cites W4292363360 @default.
- W2928758868 cites W72690721 @default.
- W2928758868 cites W2081622708 @default.
- W2928758868 doi "https://doi.org/10.1007/978-3-030-38617-7_15" @default.
- W2928758868 hasPublicationYear "2020" @default.
- W2928758868 type Work @default.
- W2928758868 sameAs 2928758868 @default.
- W2928758868 citedByCount "0" @default.
- W2928758868 crossrefType "book-chapter" @default.
- W2928758868 hasAuthorship W2928758868A5022689387 @default.