Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

Journal of Applied Remote Sensing

Volumn 4, Issue 1, 2010, Pages

  Yang, He ^a   Ma, Ben ^a   Du, Qian ^a   Yang, Chenghai ^b  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b KIKA DE LA GARZA SUBTROPICAL AGRICULTURAL RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION; IMAGE RESOLUTION; INFORMATION USE; LAND USE; PIXELS; REMOTE SENSING; ROOFS; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CONNECTIVITY INFORMATION; CONTEXTUAL INFORMATION; HIGH SPATIAL RESOLUTION; HOMOGENEOUS REGIONS; HYPER-SPECTRAL IMAGERIES; SPATIAL NEIGHBORHOOD RELATIONSHIPS; SPECTRAL VARIATION;

IMAGE ENHANCEMENT;

EID: 80055099303     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.3491192     Document Type: Article

References (19)

1. Q. Weng and D. A. Quattrochi, Urban Remote Sensing, CRC Press, Boca Raton, FL(2006).
2. H.-W. Chen, N.-B. Chang, R.-F. Yu, and Y.-W. Huang, "Urban land use and land coverclassification using neural-fuzzy inference approach with Formosat-2 data," J. Appl.Remote Sens. 3, 033558 (2009) [doi:10.1117/1.3265995].
3. R. E. Karlsen, D. J. Gorsich, and G. R. Gerhart, "Target classification via support vectormachine," Opt. Eng. 39, 704-711 (2000) [doi:10.1117/1.602417].
4. G. Zhu and D. G. Blumberg, "Classification using ASTER data and SVM algorithms:The case study of Beer Sheva, Israel," Remote Sens. Environ. 80, 233-240 (2002)[doi:10.1016/S0034-4257(01)00305-4]. (Pubitemid 34409346)
5. S. van der Linden, A. Janz, B. Waske, M. Eiden, and P. Hostert, "Classifying segmentedhyperspectral data from a heterogeneous urban environment using support vectormachines," J. Appl. Remote Sens. 1, 013543 (2007) [doi:10.1117/1.2813466].
6. R. V. Platt and A. F. H. Goetz, "A Comparison of AVIRIS and Landsat for land useclassification at the urban fringe," Photogram. Eng. Remote Sens. 70, 813-819 (2004). (Pubitemid 39081752)
7. F. Dell' Acqua, P. Gamba, A. Ferari, J. A. Palmason, J. A. Benediktsson, and K.Arnason, "Exploiting spectral and spatial information in hyperspectral urban data withhigh resolution," IEEE Geosci. Remote Sens. Lett. 1, 322-326 (2004)[doi:10.1109/LGRS.2004.837009].
8. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectraldata from urban areas based on extended morphological profiles," IEEE Trans. Geosci.Remote Sens. 43, 480-491 (2005) [doi:10.1109/TGRS.2004.842478]. (Pubitemid 40320270)
9. J. Stuckens, P. R. Coppin, and M. E. Bauer, "Integrating contextual information withper-pixel classification for improved land cover classification," Remote Sens. Environ.71, 282-296 (2000) [doi:10.1016/S0034-4257(99)00083-8]. (Pubitemid 30124726)
10. T. Kasetkasem, M. K. Arora, and P. K. Varshney, "Super-resolution land cover mappingusing a Markov random field based approach," Remote Sens. Environ. 96, 302-314(2005) [doi:10.1016/j.rse.2005.02.006]. (Pubitemid 40912432)
11. B. Zhang, X. Jia, Z. Chen, and Q. Tong, "A patch-based image classification byintegrating hyperspectral data with GIS," Int. J. Remote Sens. 27, 3337-3346 (2006)[doi:10.1080/01431160500409577]. (Pubitemid 44203584)
12. X. Huang and L. Zhang, "Object-oriented subspace analysis for airborne hyperspectralremote sensing imagery," Neurocomput. 73, 927-936 (2010)[doi:10.1016/j.neucom.2009.09.011].
13. R. L. Kettig and D. A. Landgrebe, "Classification of multispectral image data byextraction and classification of homogeneous objects," IEEE Trans. Geosci. Electron.GE-14, 19-26 (1976) [doi:10.1109/TGE.1976.294460].
14. X. Huang and L. Zhang, "An adaptive mean-shift analysis approach for object extractionand classification from urban hyperspectral imagery," IEEE Trans. Geosci. Remote Sens.46, 4173-4185 (2008) [doi:10.1109/TGRS.2008. 2002577].
15. D. Forster, Y. Buehler, and T. W. Kellenberger, "Mapping urban and peri-urbanagriculture using high spatial resolution satellite data," J. Appl. Remote Sens. 3, 033523(2009) [doi:10.1117/1.3122364].
16. eCognition Professional User Guide 4, Munich, Germany,(2003),http://www. definiens-imaging.com
17. M. Baatz and A. Schäpe, "Multiresolution segmentation-an optimization approach forhigh qualitymulti-scale image segmentation," (2000),http://www.ecognition.com/document/multiresolution-segmentation- optimization-approach-high-quality-multi-scale-image-segmentat
18. G. J. Hay, T. Blaschke, D. J. Marceau, and A. Bouchard, "A comparison of three image-object methods for the multiscale analysis of landscape structure," ISPRS J. Photogram.Remote Sens. 57, 327-345 (2003) [doi:10.1016/S0924-2716(02)00162-4]. (Pubitemid 36391580)
19. L. Bruzzone and L. Carlin, "A multilevel context-based system for classification of veryhigh spatial resolution images," IEEE Trans. Geosci. Remote Sens.44, 2587-2600 (2006)[doi:10.1109/TGRS.2006.875360]. (Pubitemid 44321552)