Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 3,402

The DOI matches its Crossref record.

A survey of image classification methods and techniques for improving classification performance

Lu, D., Weng, Q.·2007·International Journal of Remote Sensing
DOI 10.1080/01431160600746456
Open this paper in CoChat — free account

Abstract

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non‐parametric classifiers such as neural network, decision tree classifier, and knowledge‐based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image‐processing chain to improve classification accuracy.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/01431160600746456

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 376

  1. Adams , J. B. , Smith , M. O. and Gillespie , A. R. 1993 . “ Imaging spectroscopy: interpretation based on spectral mixture analysis. ” . In Remote Geochemical Analysis: Elemental and mineralogical composition , Edited by: Pieters , C. M and Englert , P.. A. J . 145 – 166 . New York : Cambridge University Press .
  2. 10.1016/0034-4257(94)00098-8 · DOI · Paper page
  3. Althausen , J. D. 2002 . “ What remote sensing system should be used to collect the data? ” . In Manual of Geospatial Science and Technology , Edited by: Bossler , J. D , Jensen , J. R , McMaster , R. B and Rizos , C . 276 – 297 . New York : Taylor and Francis .
  4. 10.1080/0143116031000115111 · doi:10.1080/0143116031000115111
  5. 10.1080/01431160210163146 · doi:10.1080/01431160210163146
  6. 10.1080/01431160310001618031 · doi:10.1080/01431160310001618031
  7. 10.1080/01431160110053176 · doi:10.1080/01431160110053176
  8. Aplin , P. , Atkinson , P. M. and Curran , P. J. 1999a . “ Per‐field classification of land use using the forthcoming very fine spatial resolution satellite sensors: problems and potential solutions. ” . In Advances in Remote Sensing and GIS Analysis , Edited by: Atkinson , P. M and Tate , N. J . 219 – 239 . New York : John Wiley and Sons .
  9. 10.1016/S0034-4257(98)00112-6 · doi:10.1016/s0034-4257(98)00112-6
  10. 10.1080/01431160110115960 · DOI · Paper page
  11. 10.1080/01431160310001654383 · DOI
  12. Atkinson , P. M. and Curran , P. J. 1997 . Choosing an appropriate spatial resolution for remote sensing investigations. . Photogrammetric Engineering and Remote Sensing , 63 : 1345 – 1351 .
  13. 10.1080/014311697218700 · DOI · Paper page
  14. 10.1080/014311697218836 · DOI · Paper page
  15. 10.1109/36.387577 · doi:10.1109/36.387577
  16. 10.1080/01431160119220 · doi:10.1080/01431160119220
  17. 10.5589/m03-014 · doi:10.5589/m03-014
  18. 10.1109/36.377929 · DOI · Paper page
  19. 10.1080/01431160110087944 · doi:10.1080/01431160110087944
  20. Barnsley , M. J. 1999 . “ Digital remote sensing data and their characteristics. ” . In Geographical Information Systems: Principles, techniques, applications, and management, 2nd edn , Edited by: Longley , P , Goodchild , M , Maguire , D. J and Rhind , D. W . 451 – 466 . New York : John Wiley and Sons .
  21. Barnsley , M. J. and Barr , S. L. 1996 . Inferring urban land use from satellite sensor images using kernel‐based spatial reclassification. . Photogrammetric Engineering and Remote Sensing , 62 : 949 – 958 .
  22. 10.1016/S0198-9715(97)10001-1 · doi:10.1016/s0198-9715(97)10001-1
  23. 10.1016/S0098-3004(01)00051-6 · DOI
  24. 10.1016/S0034-4257(95)00177-8 · DOI
  25. 10.1029/1999JD900243 · doi:10.1029/1999jd900243
  26. 10.1109/36.763301 · DOI
  27. 10.1109/36.469483 · DOI
  28. 10.1016/j.isprsjprs.2003.10.002 · DOI · Paper page
  29. 10.1016/S0098-3004(99)00119-3 · DOI
  30. 10.1016/S0098-3004(02)00033-X · doi:10.1016/s0098-3004(02)00033-x
  31. 10.1109/36.563272 · doi:10.1109/36.563272
  32. 10.1016/S0167-8655(99)00061-6 · doi:10.1016/s0167-8655(99)00061-6
  33. 10.1109/3468.477860 · DOI · Paper page
  34. Bolstad , P. V. and Lillesand , T. M. 1992 . Rule‐based classification models: flexible integration of satellite imagery and thematic spatial data. . Photogrammetric Engineering and Remote Sensing , 58 : 965 – 971 .
  35. Brisco , B. and Brown , R. J. 1995 . Multidate SAR/TM synergism for crop classification in western Canada. . Photogrammetric Engineering and Remote Sensing , 61 : 1009 – 1014 .
  36. 10.1080/07038992.1999.10874745 · doi:10.1080/07038992.1999.10874745
  37. 10.1016/S0304-3800(99)00100-3 · DOI
  38. Bruzzone , L. , Conese , C. , Maselli , F. and Roli , F. 1997 . Multisource classification of complex rural areas by statistical and neural‐network approaches. . Photogrammetric Engineering and Remote Sensing , 63 : 523 – 533 .
  39. 10.1109/36.763299 · DOI
  40. 10.2747/0749-3878.40.2.91 · doi:10.2747/0749-3878.40.2.91
  41. Canters , F. 1997 . Evaluating the uncertainty of area estimates derived from fuzzy land‐cover classification. . Photogrammetric Engineering and Remote Sensing , 63 : 403 – 414 .
  42. 10.1016/j.rse.2003.10.024 · doi:10.1016/j.rse.2003.10.024
  43. Carlotto , M. J. 1998 . Spectral shape classification of Landsat Thematic Mapper imagery. . Photogrammetric Engineering and Remote Sensing , 64 : 905 – 913 .
  44. Carr , J. R. 1999 . “ Classification of digital image texture using variograms. ” . In Advances in Remote Sensing and GIS Analysis , Edited by: Atkinson , P. M and Tate , N. J . 135 – 146 . New York : John Wiley and Sons .
  45. 10.1109/36.729366 · doi:10.1109/36.729366
  46. Chalifoux , S. , Cavayas , F. and Gray , J. T. 1998 . Mapping‐guided approach for the automatic detection on Landsat TM images of forest stands damaged by the spruce budworm. . Photogrammetric Engineering and Remote Sensing , 64 : 629 – 635 .
  47. Chavez , P. S. Jr. 1996 . Image‐based atmospheric corrections—revisited and improved. . Photogrammetric Engineering and Remote Sensing , 62 : 1025 – 1036 .
  48. 10.1016/S0924-2716(03)00014-5 · doi:10.1016/s0924-2716(03)00014-5
  49. Chen , D. and Stow , D. A. 2002 . The effect of training strategies on supervised classification at different spatial resolution. . Photogrammetric Engineering and Remote Sensing , 68 : 1155 – 1162 .
  50. 10.14358/PERS.69.11.1279 · doi:10.14358/pers.69.11.1279
  51. 10.1080/01431160310001618464 · doi:10.1080/01431160310001618464
  52. Chen , K. S. , Tzeng , Y. C. , Chen , C. F. and Kao , W. L. 1995 . Land‐cover classification of multispectral imagery using a dynamic learning neural network. . Photogrammetric Engineering and Remote Sensing , 61 : 403 – 408 .
  53. 10.1080/014311697218746 · doi:10.1080/014311697218746
  54. 10.1080/0143116032000160480 · doi:10.1080/0143116032000160480
  55. 10.1080/014311600210092 · DOI · Paper page
  56. 10.1080/014311698214451 · doi:10.1080/014311698214451
  57. 10.1016/j.rse.2004.05.008 · DOI
  58. Civco , D. L. 1989 . Topographic normalization of Landsat Thematic Mapper digital imagery. . Photogrammetric Engineering and Remote Sensing , 55 : 1303 – 1309 .
  59. Colby , J. D. 1991 . Topographic normalization in rugged terrain. . Photogrammetric Engineering and Remote Sensing , 57 : 531 – 537 .
  60. 10.1080/0143116031000150095 · doi:10.1080/0143116031000150095
  61. 10.1080/02757259409532223 · doi:10.1080/02757259409532223
  62. 10.1016/0034-4257(91)90048-B · DOI · Paper page
  63. Congalton , R. G. and Green , K. 1993 . A practical look at the sources of confusion in error matrix generation. . Photogrammetric Engineering and Remote Sensing , 59 : 641 – 644 .
  64. Congalton , R. G. and Green , K. 1999 . Assessing the Accuracy of Remotely Sensed Data: Principles and practices , Boca Raton, London, New York : Lewis Publishers .
  65. Congalton , R. G. and Mead , R. A. 1983 . A quantitative method to test for consistency and correctness in photo interpretation. . Photogrammetric Engineering and Remote Sensing , 49 : 69 – 74 .
  66. Congalton , R. G. and Plourde , L. 2002 . “ Quality assurance and accuracy assessment of information derived from remotely sensed data. ” . In Manual of Geospatial Science and Technology , Edited by: Bossler , J . 349 – 361 . London : Taylor & Francis .
  67. 10.1080/014311697218403 · doi:10.1080/014311697218403
  68. 10.1080/014311698215379 · doi:10.1080/014311698215379
  69. 10.1080/014311698214848 · DOI · Paper page
  70. 10.1109/36.763293 · doi:10.1109/36.763293
  71. 10.1016/S0034-4257(01)00184-5 · DOI
  72. 10.1080/01431168708948612 · doi:10.1080/01431168708948612
  73. 10.1080/014311698213911 · doi:10.1080/014311698213911
  74. 10.1080/01431160210155910 · doi:10.1080/01431160210155910
  75. Debeir , O. , van den Steen , I. , Latinne , P. , van Ham , P. and Wolff , E. 2002 . Textural and contextual land‐cover classification using single and multiple classifier systems. . Photogrammetric Engineering and Remote Sensing , 68 : 597 – 605 .
  76. 10.1016/S0034-4257(00)00142-5 · DOI · Paper page
  77. 10.1080/014311698214235 · DOI · Paper page
  78. 10.1016/S0034-4257(03)00135-4 · DOI · Paper page
  79. 10.1016/S0031-3203(99)00215-0 · doi:10.1016/s0031-3203(99)00215-0
  80. 10.1016/S0031-3203(02)00065-1 · doi:10.1016/s0031-3203(02)00065-1
  81. 10.1016/S0034-4257(02)00029-9 · DOI · Paper page
  82. Dungan , J. L. 2002 . “ Toward a comprehensive view of uncertainty in remote sensing analysis. ” . In Uncertainty in Remote Sensing and GIS , Edited by: Foody , G. M and Atkinson , P. M . 25 – 35 . Chichester : John Wiley & Sons . · DOI
  83. 10.1109/36.789656 · doi:10.1109/36.789656
  84. 10.1109/TGRS.1990.573019 · doi:10.1109/tgrs.1990.573019
  85. Ehlers , M. , Edwards , G. and Bedard , Y. 1989 . Integration of remote sensing with geographic information systems: a necessary evolution. . Photogrammetric Engineering and Remote Sensing , 55 : 1619 – 1627 .
  86. Ekstrand , S. 1996 . Landsat TM‐based forest damage assessment: correction for topographic effects. . Photogrammetric Engineering and Remote Sensing , 62 : 151 – 161 .
  87. Emerson , C. W. , Lam , N. S. and Quattrochi , D. A. 1999 . Multi‐scale fractal analysis of image texture and pattern. . Photogrammetric Engineering and Remote Sensing , 65 : 51 – 61 .
  88. 10.1080/01431160210145597 · doi:10.1080/01431160210145597
  89. Epstein , J. , Payne , K. and Kramer , E. 2002 . Techniques for mapping suburban sprawl. . Photogrammetric Engineering and Remote Sensing , 68 : 913 – 918 .
  90. 10.1080/0143116031000150077 · DOI
  91. 10.1016/j.rse.2004.04.006 · DOI
  92. 10.1080/01431169608949156 · doi:10.1080/01431169608949156
  93. 10.1080/014311698216008 · doi:10.1080/014311698216008
  94. Estes , J. E. and Loveland , T. R. 1999 . “ Characteristics, sources, and management of remotely‐sensed data. ” . In Geographical Information Systems: Principles, Techniques, Applications, and Management, 2nd edn , Edited by: Longley , P , Goodchild , M , Maguire , D. J and Rhind , D. W . 667 – 675 . New York : John Wiley and Sons .
  95. 10.1016/j.rse.2002.06.006 · DOI
  96. 10.1080/01431160210133563 · doi:10.1080/01431160210133563
  97. 10.1080/02693799308901966 · doi:10.1080/02693799308901966
  98. 10.1080/014311697219015 · DOI · Paper page
  99. 10.1080/014311697216649 · doi:10.1080/014311697216649
  100. Foody , G. M. 1992 . On the compensation for chance agreement in image classification accuracy assessment. . Photogrammetric Engineering and Remote Sensing , 58 : 1459 – 1460 .
  101. 10.1080/01431169608948706 · DOI · Paper page
  102. 10.1080/014311698214659 · DOI
  103. Foody , G. M. 1999 . “ Image classification with a neural network: from completely‐crisp to fully‐fuzzy situation. ” . In Advances in Remote Sensing and GIS Analysis , Edited by: Atkinson , P. M and Tate , N. J . 17 – 37 . New York : John Wiley and Sons .
  104. 10.1080/01431160110109570 · doi:10.1080/01431160110109570
  105. 10.1016/S0034-4257(01)00295-4 · DOI · Paper page
  106. 10.14358/PERS.70.5.627 · DOI · Paper page
  107. 10.1080/01431160310001648019 · doi:10.1080/01431160310001648019
  108. 10.1080/014311697218764 · DOI · Paper page
  109. 10.1002/0470035269 · DOI
  110. 10.1080/01431169408954100 · DOI · Paper page
  111. 10.1109/TGRS.2004.827257 · DOI · Paper page
  112. 10.1016/j.rse.2004.06.017 · DOI · Paper page
  113. Foody , G. M. , McCulloch , M. B. and Yates , W. B. 1995 . Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. . Photogrammetric Engineering and Remote Sensing , 61 : 391 – 401 .
  114. Foschi , P. G. and Smith , D. K. 1997 . Detecting subpixel woody vegetation in digital imagery using two artificial intelligence approaches. . Photogrammetric Engineering and Remote Sensing , 63 : 493 – 500 .
  115. Franklin , J. , Phinn , S. R. , Woodcock , C. E. and Rogan , J. 2003 . “ Rationale and conceptual framework for classification approaches to assess forest resources and properties. ” . In Remote Sensing of Forest Environments: Concepts and case studies , Edited by: Wulder , M. A and Franklin , S. E . 279 – 300 . Boston : Kluwer Academic Publishers . · doi:10.1007/978-1-4615-0306-4_10
  116. 10.1201/9781420032857 · DOI · Paper page
  117. 10.1080/01431168908903979 · DOI
  118. 10.1080/01431169008955039 · doi:10.1080/01431169008955039
  119. 10.1191/0309133302pp332ra · DOI
  120. Franklin , S. E. , Connery , D. R. and Williams , J. A. 1994 . Classification of alpine vegetation using Landsat Thematic Mapper, SPOT HRV and DEM data. . Canadian Journal of Remote Sensing , 20 : 49 – 56 .
  121. 10.1016/0098-3004(96)00009-X · DOI
  122. 10.1080/01431160110113971 · doi:10.1080/01431160110113971
  123. 10.1016/S0034-4257(97)00049-7 · DOI · Paper page
  124. 10.1109/36.752215 · DOI · Paper page
  125. Friedl , M. A. , McGwire , K. C. and McIver , D. K. 2001 . “ An overview of uncertainty in optical remotely sensed data for ecological applications. ” . In Spatial Uncertainty in Ecology: Implications for remote sensing and GIS applications , Edited by: Hunsaker , C. T , Goodchild , M. F , Friedl , M. A and Case , T. J . 258 – 283 . New York : Springer‐Verlag . · DOI
  126. 10.1016/S0924-2716(00)00018-6 · DOI
  127. 10.1080/01431160310001619607 · DOI · Paper page
  128. 10.1016/S0034-4257(98)00115-1 · DOI
  129. Garguet‐Duport , B. , Girel , J. , Chassery , J. and Pautou , G. 1996 . The use of multiresolution analysis and wavelet transform for merging SPOT panchromatic and multispectral image data. . Photogrammetric Engineering and Remote Sensing , 62 : 1057 – 1066 .
  130. 10.1080/01431160210144499 · doi:10.1080/01431160210144499
  131. 10.1080/01431169408954228 · doi:10.1080/01431169408954228
  132. 10.1016/j.rse.2004.06.006 · doi:10.1016/j.rse.2004.06.006
  133. 10.1016/j.rse.2003.07.010 · DOI · Paper page
  134. 10.1080/07038992.1994.10874578 · doi:10.1080/07038992.1994.10874578
  135. Gong , P. 1996 . Integrated analysis of spatial data from multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping. . Photogrammetric Engineering and Remote Sensing , 62 : 513 – 523 .
  136. Gong , P. and Howarth , P. J. 1992 . Frequency‐based contextual classification and gray‐level vector reduction for land‐use identification. . Photogrammetric Engineering and Remote Sensing , 58 : 423 – 437 .
  137. 10.1016/0034-4257(92)90011-8 · DOI
  138. Gopal , S. and Woodcock , C. 1994 . Theory and methods for accuracy assessment of thematic maps using fuzzy sets. . Photogrammetric Engineering and Remote Sensing , 60 : 181 – 188 .
  139. 10.1080/01431168608954686 · doi:10.1080/01431168608954686
  140. 10.1080/01431169608948987 · doi:10.1080/01431169608948987
  141. 10.1016/S0034-4257(97)00177-6 · doi:10.1016/s0034-4257(97)00177-6
  142. 10.1080/0143116021000021288 · DOI
  143. Haack , B. N. , Solomon , E. K. , Bechdol , M. A. and Herold , N. D. 2002 . Radar and optical data comparison/integration for urban delineation: a case study. . Photogrammetric Engineering and Remote Sensing , 68 : 1289 – 1296 .
  144. 10.1016/j.rse.2003.10.002 · doi:10.1016/j.rse.2003.10.002
  145. 10.1080/01431160310001647993 · doi:10.1080/01431160310001647993
  146. 10.14358/PERS.69.7.785 · doi:10.14358/pers.69.7.785
  147. 10.1080/01431169608949069 · DOI · Paper page
  148. 10.1109/TSMC.1973.4309314 · DOI · Paper page
  149. Hardin , P. J. 1994 . Parametric and nearest‐neighbor methods for hybrid classification: a comparison of pixel assignment accuracy. . Photogrammetric Engineering and Remote Sensing , 60 : 1439 – 1448 .
  150. Hardin , P. J. and Shumway , J. M. 1997 . Statistical significance and normalized confusion matrices. . Photogrammetric Engineering and Remote Sensing , 63 : 735 – 740 .
  151. Harris , P. M. and Ventura , S. J. 1995 . The integration of geographic data with remotely sensed imagery to improve classification in an urban area. . Photogrammetric Engineering and Remote Sensing , 61 : 993 – 998 .
  152. 10.1016/0034-4257(95)00189-1 · DOI
  153. 10.1109/TGRS.1990.572934 · DOI · Paper page
  154. 10.1080/01431160050029495 · DOI
  155. Heo , J. and FitzHugh , T. W. 2000 . A standardized radiometric normalization method for change detection using remotely sensed imagery. . Photogrammetric Engineering and Remote Sensing , 66 : 173 – 182 .
  156. 10.14358/PERS.69.9.991 · DOI · Paper page
  157. 10.1080/02693799608902114 · doi:10.1080/02693799608902114
  158. Hinton , J. C. 1999 . “ Image classification and analysis using integrated GIS. ” . In Advances in Remote Sensing and GIS Analysis , Edited by: Atkinson , P. M and Tate , N. J . 207 – 218 . New York : John Wiley and Sons .
  159. 10.14358/PERS.69.9.973 · DOI
  160. 10.1016/0034-4257(95)00138-7 · doi:10.1016/0034-4257(95)00138-7
  161. 10.1109/72.991427 · DOI · Paper page
  162. 10.1080/01431160110040323 · DOI · Paper page
  163. 10.14358/PERS.70.4.415 · doi:10.14358/pers.70.4.415
  164. 10.1016/S0034-4257(00)00165-6 · doi:10.1016/s0034-4257(00)00165-6
  165. Hudson , W. D. and Ramm , C. W. 1987 . Correct formulation of the Kappa coefficient of agreement. . Photogrammetric Engineering and Remote Sensing , 53 : 421 – 422 .
  166. 10.1109/TIT.1968.1054102 · DOI · Paper page
  167. Huguenin , R. L. , Karaska , M. A. , Blaricom , D. V. and Jensen , J. R. 1997 . Subpixel classification of Bald Cypress and Tupelo Gum trees in Thematic Mapper imagery. . Photogrammetric Engineering and Remote Sensing , 63 : 717 – 725 .
  168. Hung , M. and Ridd , M. K. 2002 . A subpixel classifier for urban land‐cover mapping based on a maximum‐likelihood approach and expert system rules. . Photogrammetric Engineering and Remote Sensing , 68 : 1173 – 1180 .
  169. 10.1007/978-1-4613-0209-4 · DOI
  170. 10.1016/j.rse.2003.04.004 · DOI
  171. Hutchinson , C. F. 1982 . Techniques for combining Landsat and ancillary data for digital classification improvement. . Photogrammetric Engineering and Remote Sensing , 48 : 123 – 130 .
  172. 10.1080/01431168508948285 · doi:10.1080/01431168508948285
  173. 10.1080/07038992.1997.10855208 · doi:10.1080/07038992.1997.10855208
  174. 10.1109/36.387590 · doi:10.1109/36.387590
  175. Janssen , L. F. J. and van der Wel , F. J. M. 1994 . Accuracy assessment of satellite derived land‐cover data: a review. . Photogrammetric Engineering and Remote Sensing , 60 : 419 – 426 .
  176. Janssen , L. F. , Jaarsma , M. N. and van der Linden , E. T. M. 1990 . Integrating topographic data with remote sensing for land‐cover classification. . Photogrammetric Engineering and Remote Sensing , 56 : 1503 – 1506 .
  177. Jensen , J. R. 1996 . Introduction to Digital Image Processing: A remote sensing perspective, 2nd edn , Piscataway, NJ : Prentice Hall .
  178. Jensen , J. R. and Cowen , D. C. 1999 . Remote sensing of urban/suburban infrastructure and socioeconomic attributes. . Photogrammetric Engineering and Remote Sensing , 65 : 611 – 622 .
  179. 10.1109/36.763278 · doi:10.1109/36.763278
  180. 10.1016/j.rse.2004.03.016 · doi:10.1016/j.rse.2004.03.016
  181. 10.1109/36.763300 · DOI
  182. 10.1016/S0034-4257(02)00172-4 · DOI
  183. 10.1080/07038992.1997.10855203 · DOI
  184. 10.1080/01431169408954132 · doi:10.1080/01431169408954132
  185. 10.1080/014311698215199 · doi:10.1080/014311698215199
  186. 10.1016/0167-8655(82)90050-2 · DOI
  187. 10.1080/0143116031000114851 · DOI · Paper page
  188. 10.1016/S0034-4257(03)00130-5 · DOI
  189. 10.1016/S0031-3203(03)00175-4 · DOI · Paper page
  190. 10.1016/S0034-4257(02)00133-5 · DOI · Paper page
  191. 10.1080/02757250109532431 · DOI
  192. 10.1080/0143116031000107868 · DOI
  193. 10.1080/01431169608949132 · DOI
  194. 10.1080/02693799308901955 · doi:10.1080/02693799308901955
  195. 10.1080/10106049908542127 · doi:10.1080/10106049908542127
  196. 10.1080/01431160050505874 · doi:10.1080/01431160050505874
  197. 10.1002/0471723800 · DOI · Paper page
  198. 10.1016/j.rse.2004.01.007 · DOI · Paper page
  199. Leckie , D. G. 1998 . “ Forestry applications using imaging radar. ” . In Principles and Applications of Imaging Radar, Manual of Remote Sensing, 3rd edn , Edited by: Henderson , F. M and Lewis , A. J . Vol. 2 , 437 – 509 . New York : John Wiley and Sons .
  200. 10.1109/34.879789 · DOI

200 of 376 shown

Cited by

Metadata

Authors
Lu, D., & Weng, Q.
Venue
International Journal of Remote Sensing
Publisher
Informa UK Limited
Published
2007-03
Volume
28
Issue
5
Pages
823-870
ISSN
  • 0143-1161 (ISSN-L)
  • 1366-5901
DOI
10.1080/01431160600746456
OpenAlex
W2168809519
Cited by
3,402
References
0
FWCI
153.27
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.