Evaluation of the usefulness of texture measures for crop type classification by hyperion data

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 37, Issue , 2008, Pages 999-1006

  Ashoori, H ^a   Fahimnejad, H ^a   Alimohammadi, A ^a   Soofbaf, S R ^a  

^a K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Classification; Hyperion; Hyperspectral; Linear spectral unmixing; Texture quantization

Indexed keywords

AGRICULTURE; CLASSIFICATION (OF INFORMATION); CROPS; MAXIMUM LIKELIHOOD; REMOTE SENSING;

AGRICULTURAL MANAGEMENT; CROP TYPE CLASSIFICATION; GRAY LEVEL CO-OCCURRENCE MATRIX; HYPERION; HYPERSPECTRAL; LINEAR SPECTRAL UNMIXING; MAXIMUM LIKELIHOOD CLASSIFIERS; TEXTURE QUANTIZATION;

MATHEMATICAL TRANSFORMATIONS;

EID: 84978145069     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Chica-Olmo, M., Abarca-HernaÂndez, F., 2000. Computing geostatistical image texture for remotely sensed data classification, Computers & Geosciences 26, pp 373-383.
2. Datt, B., McVicar, T. R., Van Niel, T. G., Jupp, D. L. B., and Pearlman, J. S., 2003. Preprocessing EO-1 Hyperion Hyperspectral Data to Support the Application of Agricultural Indexes. IEEE Trans. Geosci. Remote Sensing, 41(2), pp. 1246-1259.
3. Goodenough, D. G., Dyk, A., Niemann, O., Pearlman, J. S., Chen, H., Han, T., Murdoch, M., and West, C., 2003. Processing HYPERION and ALI for Forest Classification. IEEE Trans. Geosci. Remote Sensing, 41(2), pp. 1321-1331.
4. Haralick, R. M., Shanmugam, K., Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp 610-621.
5. Staenz, K., Neville R. A., H. P. and White, S., 2002. Retrieval Of Surface Reflectance From Hyperion Radiance Data, IEEE International Geoscience and Remote Sensing Symposium.
6. Castleman, K. R., 1996. Digital Image Processing. Prentice-Hall.
7. Chain-I, Chang, 2003. Hyperspectral imaging: techniques for spectral detection and classification, Kluwer academic/plenum Publisher, Newyork, N. Y.
8. Pratt, 2001. Digital Image Processing, JOHN WILEY & SONS.
9. Theodoridis, S., 1999. Pattern Recognition. Academic Press.
10. Ashoori, H., Alimohammadi, A., Valadan Zoej, M. J., Mojarradi, B., 2006. Generating Image-based Features for Improving Classification Accuracy of High Resolution Images, May, ISPRS Mid-term Symposium, Netherlands.
11. Beck R, 2003. EO-1 User Guide v. 2.3. Department of Geography University of Cincinnati.
12. Fahimnejad, H., Soofbaf, S. R., Alimohammadi, A., Valadan Zoej, M. J., 2007. Crop types classification by Hyperion data and unmixing algorithm", MapWorldForum.
13. FLAASH Module User's Guide, ENVI FLAASH Version 4.2 August, 2005 Edition.
14. Han, T., Goodenough, D. G., Dyk, A., and Love, J. 2002. Detection and correction of abnormal pixels in Hyperion image. IGARSS, vol. III, Toronto, ON, Canada, pp. 1327-1330.
15. Pearlman J. S., 2003. Hyperion Validation Report. Boeing Report Number 03-ANCOS-001.
16. th WCSS, 12-21 August, Thailand, Symposium no. 52.
17. http://www.geo.ed.ac.uk (accessed 10 Jan. 2002)