FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2766528611> ?p ?o ?g. }

• W2766528611 endingPage "346" @default.
• W2766528611 startingPage "333" @default.
• W2766528611 abstract "Hyperspectral unmixing is an important technique for remote sensing image exploitation. It aims to decompose a mixed pixel into a collection of spectrally pure components (called endmembers), and their corresponding proportions (called fractional abundances). In recent years, many studies have revealed that unmixing using spectral information alone does not sufficiently incorporate the spatial information in the remotely sensed hyperspectral image, as the pixels are treated as isolated entities without taking into account the existing local correlation among them. To address this issue, several spatial preprocessing methods have been developed to include spatial information in the spectral unmixing process. In this paper, we present a new spatial preprocessing method which presents several advantages over existing methods. The proposed method is derived from the Simple Linear Iterative Clustering (SLIC) method, which adapts the global search scope of the clustering into local regions. As a result, the spatial correlation and the spectral similarity are intrinsically incorporated at the clustering step, which results in O(N) computational complexity of the clustering procedure with N being the number of pixels in the image. First, a regional clustering is iteratively performed by using spatial and spectral information simultaneously. The obtained result is a set of clustered partitions that exhibit both spectral similarity and spatial correlation. Then, for each partition we select a subset of candidate pixels with high spectral purity. Finally, the obtained candidate pixels are gathered together and fed to a spectral-based endmember extraction method to extract the final endmembers and their corresponding fractional abundances. Our newly developed method naturally integrates the spatial and the spectral information to retain the most relevant endmember candidates. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the proposed method can obtain accurate unmixing results with less than 0.5% of the number of pixels used by other state-of-the-art methods. This confirms the advantages of integrating spatial and spectral information for hyperspectral unmixing purposes." @default.
• W2766528611 created "2017-11-10" @default.
• W2766528611 creator A5013690148 @default.
• W2766528611 creator A5036283525 @default.
• W2766528611 creator A5050455072 @default.
• W2766528611 creator A5054292278 @default.
• W2766528611 date "2018-01-01" @default.
• W2766528611 modified "2023-10-16" @default.
• W2766528611 title "Regional clustering-based spatial preprocessing for hyperspectral unmixing" @default.
• W2766528611 cites W1968258303 @default.
• W2766528611 cites W1971876701 @default.
• W2766528611 cites W1972293418 @default.
• W2766528611 cites W1998917483 @default.
• W2766528611 cites W1999478155 @default.
• W2766528611 cites W2007735486 @default.
• W2766528611 cites W2008337397 @default.
• W2766528611 cites W2023307693 @default.
• W2766528611 cites W2024738011 @default.
• W2766528611 cites W2025389829 @default.
• W2766528611 cites W2027878671 @default.
• W2766528611 cites W2029144047 @default.
• W2766528611 cites W2032861822 @default.
• W2766528611 cites W2037328426 @default.
• W2766528611 cites W2042348866 @default.
• W2766528611 cites W2053409082 @default.
• W2766528611 cites W2059976262 @default.
• W2766528611 cites W2060147006 @default.
• W2766528611 cites W2070424424 @default.
• W2766528611 cites W2070781258 @default.
• W2766528611 cites W2071190035 @default.
• W2766528611 cites W2078204800 @default.
• W2766528611 cites W2082827499 @default.
• W2766528611 cites W2085918337 @default.
• W2766528611 cites W2094304765 @default.
• W2766528611 cites W2095343758 @default.
• W2766528611 cites W2097153652 @default.
• W2766528611 cites W2102159812 @default.
• W2766528611 cites W2110456190 @default.
• W2766528611 cites W2117741752 @default.
• W2766528611 cites W2118246710 @default.
• W2766528611 cites W2119317657 @default.
• W2766528611 cites W2119531662 @default.
• W2766528611 cites W2121905192 @default.
• W2766528611 cites W2121947440 @default.
• W2766528611 cites W2123907688 @default.
• W2766528611 cites W2124260943 @default.
• W2766528611 cites W2125298866 @default.
• W2766528611 cites W2125865984 @default.
• W2766528611 cites W2128090514 @default.
• W2766528611 cites W2130939260 @default.
• W2766528611 cites W2131697388 @default.
• W2766528611 cites W2136625467 @default.
• W2766528611 cites W2136635809 @default.
• W2766528611 cites W2143457518 @default.
• W2766528611 cites W2143668817 @default.
• W2766528611 cites W2144881411 @default.
• W2766528611 cites W2146449740 @default.
• W2766528611 cites W2153885347 @default.
• W2766528611 cites W2155919893 @default.
• W2766528611 cites W2156220628 @default.
• W2766528611 cites W2157321686 @default.
• W2766528611 cites W2159411209 @default.
• W2766528611 cites W2163886442 @default.
• W2766528611 cites W2165755981 @default.
• W2766528611 cites W2169466597 @default.
• W2766528611 cites W2170406089 @default.
• W2766528611 cites W2291033752 @default.
• W2766528611 cites W2334951050 @default.
• W2766528611 cites W2336230670 @default.
• W2766528611 cites W2336417938 @default.
• W2766528611 cites W4233760599 @default.
• W2766528611 doi "https://doi.org/10.1016/j.rse.2017.10.020" @default.
• W2766528611 hasPublicationYear "2018" @default.
• W2766528611 type Work @default.
• W2766528611 sameAs 2766528611 @default.
• W2766528611 citedByCount "74" @default.
• W2766528611 countsByYear W27665286112018 @default.
• W2766528611 countsByYear W27665286112019 @default.
• W2766528611 countsByYear W27665286112020 @default.
• W2766528611 countsByYear W27665286112021 @default.
• W2766528611 countsByYear W27665286112022 @default.
• W2766528611 countsByYear W27665286112023 @default.
• W2766528611 crossrefType "journal-article" @default.
• W2766528611 hasAuthorship W2766528611A5013690148 @default.
• W2766528611 hasAuthorship W2766528611A5036283525 @default.
• W2766528611 hasAuthorship W2766528611A5050455072 @default.
• W2766528611 hasAuthorship W2766528611A5054292278 @default.
• W2766528611 hasConcept C103278499 @default.
• W2766528611 hasConcept C105611402 @default.
• W2766528611 hasConcept C115961682 @default.
• W2766528611 hasConcept C150060386 @default.
• W2766528611 hasConcept C153180895 @default.
• W2766528611 hasConcept C154945302 @default.
• W2766528611 hasConcept C159078339 @default.
• W2766528611 hasConcept C159620131 @default.
• W2766528611 hasConcept C160633673 @default.
• W2766528611 hasConcept C205649164 @default.
• W2766528611 hasConcept C34736171 @default.

• next