Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-12

AUTHORS

Sen Jia, Yao Xie, Guihua Tang, Jiasong Zhu

ABSTRACT

Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or S3RC, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of S3RC, named FS3RC. Experimental results have shown that both the proposed SRC-based approaches, S3RC and FS3RC, could achieve better performance than the other state-of-the-art methods. More... »

PAGES

4659-4668

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00500-014-1505-4

DOI

http://dx.doi.org/10.1007/s00500-014-1505-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023686044


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0909", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geomatic Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Shenzhen University", 
          "id": "https://www.grid.ac/institutes/grid.263488.3", 
          "name": [
            "The Shenzhen Key Laboratory of Spatial Information Smarting Sensing and Services, Shenzhen University, Shenzhen, China", 
            "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jia", 
        "givenName": "Sen", 
        "id": "sg:person.07774604370.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07774604370.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shenzhen University", 
          "id": "https://www.grid.ac/institutes/grid.263488.3", 
          "name": [
            "The Shenzhen Key Laboratory of Spatial Information Smarting Sensing and Services, Shenzhen University, Shenzhen, China", 
            "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xie", 
        "givenName": "Yao", 
        "id": "sg:person.012312764433.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012312764433.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shenzhen University", 
          "id": "https://www.grid.ac/institutes/grid.263488.3", 
          "name": [
            "The Shenzhen Key Laboratory of Spatial Information Smarting Sensing and Services, Shenzhen University, Shenzhen, China", 
            "College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tang", 
        "givenName": "Guihua", 
        "id": "sg:person.014131116135.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014131116135.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shenzhen University", 
          "id": "https://www.grid.ac/institutes/grid.263488.3", 
          "name": [
            "The Shenzhen Key Laboratory of Spatial Information Smarting Sensing and Services, Shenzhen University, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhu", 
        "givenName": "Jiasong", 
        "id": "sg:person.013333003037.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013333003037.96"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-1-4419-9170-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004126947", 
          "https://doi.org/10.1007/978-1-4419-9170-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4419-9170-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004126947", 
          "https://doi.org/10.1007/978-1-4419-9170-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431160600746456", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041894571"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rse.2007.07.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048284262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2012.07.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052568802"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2012.07.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052568802"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jproc.2012.2197589", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061297655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/lgrs.2004.837009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061358165"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2003.811816", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061608822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2004.842478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061609383"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2011.2129595", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061611816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2011.2162649", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061611993"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2011.2172617", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061612113"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2012.2228275", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061612687"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2005.858979", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061650709"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2008.929958", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061652186"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2005.127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061742796"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2008.79", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061743675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s0097539792240406", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062879779"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1015706.1015720", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063148832"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/whispers.2009.5288983", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093570217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icise.2010.5689027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093706101"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/whispers.2010.5594882", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094866261"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/igarss.2008.4780108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095074947"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-12", 
    "datePublishedReg": "2016-12-01", 
    "description": "Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or S3RC, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of S3RC, named FS3RC. Experimental results have shown that both the proposed SRC-based approaches, S3RC and FS3RC, could achieve better performance than the other state-of-the-art methods.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00500-014-1505-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7194697", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1050238", 
        "issn": [
          "1432-7643", 
          "1433-7479"
        ], 
        "name": "Soft Computing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "12", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "20"
      }
    ], 
    "name": "Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery", 
    "pagination": "4659-4668", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "70d8cc3071fef105b99c30ec6fd9cd226ea5626cd37fd49cad6354df0d77e1e2"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00500-014-1505-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1023686044"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00500-014-1505-4", 
      "https://app.dimensions.ai/details/publication/pub.1023686044"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:51", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8664_00000512.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs00500-014-1505-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00500-014-1505-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00500-014-1505-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00500-014-1505-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00500-014-1505-4'


 

This table displays all metadata directly associated to this object as RDF triples.

152 TRIPLES      21 PREDICATES      49 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00500-014-1505-4 schema:about anzsrc-for:09
2 anzsrc-for:0909
3 schema:author Na8b17eb158e8496aaf05566f3343db54
4 schema:citation sg:pub.10.1007/978-1-4419-9170-6
5 https://doi.org/10.1016/j.patcog.2012.07.010
6 https://doi.org/10.1016/j.rse.2007.07.028
7 https://doi.org/10.1080/01431160600746456
8 https://doi.org/10.1109/icise.2010.5689027
9 https://doi.org/10.1109/igarss.2008.4780108
10 https://doi.org/10.1109/jproc.2012.2197589
11 https://doi.org/10.1109/lgrs.2004.837009
12 https://doi.org/10.1109/tgrs.2003.811816
13 https://doi.org/10.1109/tgrs.2004.842478
14 https://doi.org/10.1109/tgrs.2011.2129595
15 https://doi.org/10.1109/tgrs.2011.2162649
16 https://doi.org/10.1109/tgrs.2011.2172617
17 https://doi.org/10.1109/tgrs.2012.2228275
18 https://doi.org/10.1109/tit.2005.858979
19 https://doi.org/10.1109/tit.2008.929958
20 https://doi.org/10.1109/tpami.2005.127
21 https://doi.org/10.1109/tpami.2008.79
22 https://doi.org/10.1109/whispers.2009.5288983
23 https://doi.org/10.1109/whispers.2010.5594882
24 https://doi.org/10.1137/s0097539792240406
25 https://doi.org/10.1145/1015706.1015720
26 schema:datePublished 2016-12
27 schema:datePublishedReg 2016-12-01
28 schema:description Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or S3RC, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of S3RC, named FS3RC. Experimental results have shown that both the proposed SRC-based approaches, S3RC and FS3RC, could achieve better performance than the other state-of-the-art methods.
29 schema:genre research_article
30 schema:inLanguage en
31 schema:isAccessibleForFree false
32 schema:isPartOf N429984457ecd4986b200be12e47bf9a7
33 Nb313686831c5473fb697d57f0b44f2ef
34 sg:journal.1050238
35 schema:name Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery
36 schema:pagination 4659-4668
37 schema:productId N22a06b7b73764522bd1b12eca62dc7a7
38 N2fbb14722bbc4d3980805d4376ca56ef
39 Nacbfffcc878a4cb987503eb47f14a23e
40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023686044
41 https://doi.org/10.1007/s00500-014-1505-4
42 schema:sdDatePublished 2019-04-10T15:51
43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
44 schema:sdPublisher N02a8cb8646f041c980d4d0e742816c29
45 schema:url http://link.springer.com/10.1007%2Fs00500-014-1505-4
46 sgo:license sg:explorer/license/
47 sgo:sdDataset articles
48 rdf:type schema:ScholarlyArticle
49 N02a8cb8646f041c980d4d0e742816c29 schema:name Springer Nature - SN SciGraph project
50 rdf:type schema:Organization
51 N22a06b7b73764522bd1b12eca62dc7a7 schema:name doi
52 schema:value 10.1007/s00500-014-1505-4
53 rdf:type schema:PropertyValue
54 N2fbb14722bbc4d3980805d4376ca56ef schema:name dimensions_id
55 schema:value pub.1023686044
56 rdf:type schema:PropertyValue
57 N429984457ecd4986b200be12e47bf9a7 schema:issueNumber 12
58 rdf:type schema:PublicationIssue
59 N7695eb78795647f0bd852d90dcfde5da rdf:first sg:person.013333003037.96
60 rdf:rest rdf:nil
61 N912de91e641e4c36a36569f0f08237c6 rdf:first sg:person.014131116135.35
62 rdf:rest N7695eb78795647f0bd852d90dcfde5da
63 Na35e112e9cd04a84a3f44cdb3d58b317 rdf:first sg:person.012312764433.16
64 rdf:rest N912de91e641e4c36a36569f0f08237c6
65 Na8b17eb158e8496aaf05566f3343db54 rdf:first sg:person.07774604370.25
66 rdf:rest Na35e112e9cd04a84a3f44cdb3d58b317
67 Nacbfffcc878a4cb987503eb47f14a23e schema:name readcube_id
68 schema:value 70d8cc3071fef105b99c30ec6fd9cd226ea5626cd37fd49cad6354df0d77e1e2
69 rdf:type schema:PropertyValue
70 Nb313686831c5473fb697d57f0b44f2ef schema:volumeNumber 20
71 rdf:type schema:PublicationVolume
72 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
73 schema:name Engineering
74 rdf:type schema:DefinedTerm
75 anzsrc-for:0909 schema:inDefinedTermSet anzsrc-for:
76 schema:name Geomatic Engineering
77 rdf:type schema:DefinedTerm
78 sg:grant.7194697 http://pending.schema.org/fundedItem sg:pub.10.1007/s00500-014-1505-4
79 rdf:type schema:MonetaryGrant
80 sg:journal.1050238 schema:issn 1432-7643
81 1433-7479
82 schema:name Soft Computing
83 rdf:type schema:Periodical
84 sg:person.012312764433.16 schema:affiliation https://www.grid.ac/institutes/grid.263488.3
85 schema:familyName Xie
86 schema:givenName Yao
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012312764433.16
88 rdf:type schema:Person
89 sg:person.013333003037.96 schema:affiliation https://www.grid.ac/institutes/grid.263488.3
90 schema:familyName Zhu
91 schema:givenName Jiasong
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013333003037.96
93 rdf:type schema:Person
94 sg:person.014131116135.35 schema:affiliation https://www.grid.ac/institutes/grid.263488.3
95 schema:familyName Tang
96 schema:givenName Guihua
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014131116135.35
98 rdf:type schema:Person
99 sg:person.07774604370.25 schema:affiliation https://www.grid.ac/institutes/grid.263488.3
100 schema:familyName Jia
101 schema:givenName Sen
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07774604370.25
103 rdf:type schema:Person
104 sg:pub.10.1007/978-1-4419-9170-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004126947
105 https://doi.org/10.1007/978-1-4419-9170-6
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/j.patcog.2012.07.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052568802
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/j.rse.2007.07.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048284262
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1080/01431160600746456 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041894571
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1109/icise.2010.5689027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093706101
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1109/igarss.2008.4780108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095074947
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1109/jproc.2012.2197589 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061297655
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1109/lgrs.2004.837009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061358165
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1109/tgrs.2003.811816 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061608822
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1109/tgrs.2004.842478 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061609383
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1109/tgrs.2011.2129595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061611816
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1109/tgrs.2011.2162649 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061611993
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1109/tgrs.2011.2172617 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061612113
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/tgrs.2012.2228275 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061612687
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1109/tit.2005.858979 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061650709
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/tit.2008.929958 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061652186
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/tpami.2005.127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742796
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/tpami.2008.79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743675
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/whispers.2009.5288983 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093570217
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/whispers.2010.5594882 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094866261
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1137/s0097539792240406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062879779
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1145/1015706.1015720 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063148832
148 rdf:type schema:CreativeWork
149 https://www.grid.ac/institutes/grid.263488.3 schema:alternateName Shenzhen University
150 schema:name College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
151 The Shenzhen Key Laboratory of Spatial Information Smarting Sensing and Services, Shenzhen University, Shenzhen, China
152 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...