Urban land cover classification from very high resolution imagery using spectral and invariant moment shape information

Canadian Journal of Remote Sensing

Volumn 36, Issue 3, 2010, Pages 248-260

  Xu, Haiqing ^a   Li, Peijun ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; REMOTE SENSING;

IMPERVIOUS SURFACE; OBJECT-BASED CLASSIFICATIONS; SEGMENTATION RESULTS; SPECTRAL INFORMATION; SPECTRAL SIMILARITY; SPECTRAL VARIABILITY; URBAN LAND COVER CLASSIFICATION; VERY HIGH RESOLUTION;

CLASSIFICATION (OF INFORMATION);

EID: 78649988569     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m10-042     Document Type: Article

References (45)

1. Abdallah, S.M., Nebot, E.M., and Rye, D.C. 1997. Object recognition and orientation via Zernike moments. In ACCV'98: Proceedings of the 3rd Asian Conference on Computer Vision, 8-10 January 1998, Hong Kong. Edited by R.T. Chin and T.-C. Pong. Springer, Berlin. Lecture Notes in Computer Science Volume 1351, pp. 386-393.
2. Abu-Mostafa, Y.S., and Psaltis, D. 1984. Recognitive aspects of moment invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. Pami-6, No. 6, pp. 698-706.
3. Bazi, Y., and Melgani, F. 2006. Towards an optimal SVM classification system for hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 11, pp. 3374-3385. doi:10.1109/TGRS.2006.880628.
4. Bellens, R., Gautama, S., Martinez-Fonte, L., Philips, W., Chan, J.C.-W., and Canters, F. 2008. Improved classification of VHR images of urban areas using directional morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 10, pp. 2803-2813. doi:10.1109/TGRS.2008.2000628.
5. Benediktsson, J.A., Pesaresi, M., and Arnason, K. 2003. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 9, pp. 1490-1949. doi:10.1109/TGRS.2003.814625.
6. Beucher, S., and Meyer, F. 1993. The morphological approach to segmentation: the watershed transformation. In Mathematical morphology in imageprocessing. Edited by E.R. Dougherty. Marcel Dekker, New York. pp. 433-482.
7. Bleau, A., and Leon, L.J. 2000. Watershed-based segmentation and region merging. Computer Vision and Image Understanding, Vol. 77, pp. 317-370.
8. Bruzzone, L., and Carlin, L. 2006. A multilevel context-based system for classification of very high spatial resolution images. IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 9, pp. 2587-2600. doi:10.1109/TGRS.2006.875360.
9. Camps-Valls, G., Gomez-Chova, L., Mutioz-Mari, J., Vila-Frances, J., and Calpe-Maravilla, J. 2006. Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, Vol. 3, No. 1, pp. 93-97. doi:10.1109/LGRS.2005.857031.
10. Carleer, A.P., and Wolff, E. 2006. Urban land cover multi-level region-based classification of VHR data by selecting relevant features. International Journal of Remote Sensing, Vol. 27, No. 6, pp. 1035-1051. doi:10.1080/01431160500297956.
11. Carleer, A.P., Debeir, O., and Wolff, E. 2005. Assessment of very high spatial resolution satellite image segmentations. Photogrammetric Engineering & Remote Sensing, Vol. 71, No. 11, pp. 1285-1294.
12. Congalton, R.G., and Green, K. 1999. Assessing the accuracy of remotely sensed data: principles and practices. Lewis Publishers, Boca Raton, Fla. 137 pp.
13. Dai, X., and Khorram, S. 1999. A feature-based image registration algorithm using improved chain-code representation combined with invariant moments. IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, pp. 2351-2362. doi:10.1109/36.789634.
14. Elmqvist, B., Ardo, J., and Olsson, L. 2008. Land use studies in drylands: an evaluation of object-oriented classification of very high resolution panchromatic imagery. International Journal of Remote Sensing, Vol. 29, No. 24, pp. 7129-7140. doi:10.1080/01431160802238419.
15. Flusser, J., and Suk, T. 1994. A moment-based approach to registration of images with affine geometric distortion. IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 2, pp. 382-387. doi:10.1109/36.295052.
16. Foody, G.M., and Mathur, A. 2004. A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 6, pp. 1335-1343. doi:10.1109/TGRS.2004.827257.
17. Gerke, M., Heipke, C., and Straub, B.-M. 2001. Building extraction from aerial imagery using a generic scene model and invariant geometric moments. In Proceedings of the IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, 8-9 November 2001, Rome, Italy. IEEE, Piscataway, N.J. ISBN 0-7803-7059-7. pp. 85-89. doi:10.1109/DFUA.2001.985732.
18. Hu, M.K. 1962. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, Vol. 8, pp. 179-187.
19. Huang, C., Davis, L.S., and Townshend, J.R.G. 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, Vol. 23, No. 4, pp. 725-749. doi:10.1080/ 01431160110040323.
20. Jain, A.K. 1989. Fundamentals of digital image processing. Prentice-Hall Inc., Englewood Cliffs, N.J.
21. Laben, C.A., and Brower, B.V. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6011875. Available from www.freepatentsonline.com/6011875.html [accessed December 2008].
22. Lemarechal, C., Fjortoft, R., Marthon, P., and Cubero-Castan, E. 1998. Comments on “Geodesic saliency of watershed contours and hierarchical segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 7, pp. 762-766.
23. Li, P., and Xiao, X. 2007. Multispectral image segmentation by a multichannel watershed-based approach. International Journal of Remote Sensing, Vol. 28, No. 19, pp. 4429-4452. doi:10.1080/01431160601034910.
24. Li, P., Guo, J., Xu, H., and Xiao, X. 2008. Multilevel object based image classification over urban area based hierarchical image segmentation and invariant moments. In Proceedings of GEOBIA 2008 - Pixel, Objects, Intelligence: Geographical Object Based Image Analysis for the 21th Century, 6-7 August 2008, Calgary, Alta. Available from http://homepages.ucalgary.ca/-gjhay/geobia/Proceedings/Sessions/Session11/6001_Li_Proc.pdf.
25. Li, P., Guo, J., Song, B., and Xiao, X. 2010. A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. In press. Available from http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5585835.
26. Liu, H., and He, G. 2008. Shape feature extraction of high resolution remote sensing image based on SUSAN and moment invariant. In CISP 2008: Proceedings of the Congress on Image and Signal Processing, 27-30 May 2008, Sanya, China. IEEE, Piscataway, N.J. Vol. 2, pp. 801-807. doi:10.1109/CISP.2008.244.
27. Lo, C.H., and Don, H.S. 1989. 3D moment forms: their construction and application to object identification and positioning. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, pp. 1053-1064. doi:10.1109/34.42836.
28. Lu, D., and Weng, Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, Vol. 28, No. 5, pp. 823-870. doi:10.1080/01431160600746456.
29. Luo, L.M., Hamitouche, C., Dilenseger, J.L., and Coatrieux, J.L. 1993. A moment-based three-dimensional edge operator. IEEE Transactions on Biomedical Engineering, Vol. 40, pp. 693-703. doi:10.1109/10.237699.
30. Maas, H.G. 1999. Closed solutions for the determination of parametric building models from invariant moments of airborne laserscanner data. In Proceedings of the ISPRS Conference on Automatic Extraction of GIS Objects from Digital Imagery, 8-10 September 1999, Miinchen, Germany. ITC, Enschede, The Netherlands. International Archives of Photogram-metry and Remote Sensing (IAPRS), Vol. 32, No. 3-2W5, pp. 193-199.
31. Mercimek, M., Gulez, K., and Mumcu, T.V. 2005. Real object recognition using moment invariants. Sadhana, Vol. 30, No. 6, pp. 765-775. doi:10.1007/BF02716709.
32. Najman, L., and Schmitt, M. 1996. Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 12, pp. 1163-1173. doi:10.1109/34.546254.
33. Pagot, E., and Pesaresi, M. 2008. Systematic study of the urban postconflict change classification performance using spectral and structural features in a support vector machine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 1, No. 2, pp. 120-128. doi:10.1109/JSTARS.2008.2001154.
34. Pagot, E., Pesaresi, M., Buda, D., and Ehrlich, D. 2008. Development of an object-oriented classification model using very high resolution satellite imagery for monitoring diamond mining activity. International Journal of Remote Sensing, Vol. 29, No. 2, pp. 499-512. doi:10.1080/01431160601047771.
35. Plaza, A., Martinez, P., Perez, R., and Plaza, J. 2002. Spatial/spectral endmem-ber extraction by multidimensional morphological operations. IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 9, pp. 2025-2041.
36. Prismall, S.P., Nixon, M.S., and Carter, J.N. 2002. On moving object reconstruction by moments. In BMVC 2002: Electronic Proceedings of the 13th British Machine Vision Conference, 2-5 September 2002, Cardiff, Wales. Edited by P.L. Rosin and A.D. Marshall. British Machine Vision Association, Malvern, UK.
37. Richard, J.P., and Anthony, P.R. 1992. A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP: Graphical Models and Image Processing, Vol. 54, No. 5, pp. 438-460. doi:10.1016/1049-9652(92)90027-U.
38. Schmitt, M. 1998. Response to the Comment on “Geodesic Saliency of Watershed Contours and Hierarchical Segmentation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 7, pp. 764-766.
39. Shackelford, A.K., and Davis, C.H. 2003. A combined fuzzy pixel-based and object-based approach for classification of high-resolution multi-spectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 10, pp. 2354-2363.
40. Shen, D.G., and Horace, H.S. 1999. Discriminative wavelet shape descriptors for recognition of 2-D patterns. Pattern Recognition, Vol. 32, pp. 151-165. doi:10.1016/S0031-3203(98)00137-X.
41. Soille, P. 2003. Morphological image analysis: principles and applications. 2nd ed. Springer, Berlin.
42. Teague, M. 1980. Image analysis via the general theory of moments. Journal of the Optical Society of America, Vol. 70, pp. 920-930. doi:10.1364/ JOSA.70.000920.
43. Teh, C.H., and Chin, R.T. 1988. On image analysis by the methods of moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 4, pp. 496-513. doi:10.1109/34.3913.
44. Tilton, J.C. 2003. Analysis of hierarchically related image segmentations. In Proceedings of the IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 27-28 October 2003, Washington, D.C. IEEE, Piscataway, N.J. pp. 60-69.
45. Zhang, L., Huang, X., Huang, B., and Li, P. 2006. A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 10, pp. 2950-2961. doi:10.1109/TGRS.2006.876704.