Advanced Classification of Lands at TM and Envisat Images of Mongolia

DOI: 10.4236/ars.2013.22014   PDF   HTML     3,243 Downloads   5,920 Views   Citations

Abstract

The aim of this study is to fuse high resolution optical and microwave images and classify urban land cover types using a refined Mahalanobis distance classifier. For the data fusion, multiplicative method, Brovey transform, intensity-huesaturation method and principal component analysis are used and the results are compared. The refined method uses spatial thresholds defined from local knowledge and the bands defined from multiple sources. The result of the refined Mahalanobis distance method is compared with the result of a standard technique and it demonstrates a higher accuracy. Overall, the research indicates that the combined use of optical and microwave images can notably improve the interpretation and classification of land cover types and the refined Mahalanobis classification is a powerful tool to increase classification accuracy.

Share and Cite:

V. Battsengel, D. Amarsaikhan, T. Bat-Erdene, E. Egshiglen, A. Munkh-Erdene and M. Ganzorig, "Advanced Classification of Lands at TM and Envisat Images of Mongolia," Advances in Remote Sensing, Vol. 2 No. 2, 2013, pp. 102-110. doi: 10.4236/ars.2013.22014.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] D. Amarsaikhan and T. Douglas, “Data Fusion and Multisource Data Classification,” International Journal of Remote Sensing, Vol. 25, No. 17, 2004, pp. 3529-3539. doi:10.1080/0143116031000115111
[2] M. Ehlers, S. Klonus and P. J. Astrand, “Quality Assessment for Multi-Sensor Multi-Date Image Fusion,” CDROM Proceedings of ISPRS Congresses, Beijing, 3-11 July 2008, pp. 499-506.
[3] H. D. Herold and B. N. Haack, “Fusion of Radar and Optical Data for Land Cover Mapping,” Geocarto International, Vol. 17, No. 2, 2002, pp. 21-30. doi:10.1080/10106040208542232
[4] C. Pohl and J. L. Van Genderen, “Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications,” International Journal of Remote Sensing, Vol. 19, No. 5, 1998, pp. 823-854. doi:10.1080/014311698215748
[5] A. Pope, I. C. Willis, W. G. Rees, N. S. Arnold and F. Palsson, “Combining Airborne Lidar and Landsat ETM + Data with Photoclinometry to Produce a Digital Elevation Model for Langj?kull, Iceland,” International Journal of Remote Sensing, Vol. 34, No. 4, 2013, pp. 1005-1025. doi:10.1080/01431161.2012.705446
[6] J. W. Roberts, J. A. N. van Aardt and F. B. Ahmed, “Image Fusion for Enhanced Forest Structural Assessment,” International Journal of Remote Sensing, Vol. 32, No. 1, 2011, pp. 243-266.
[7] Y. Wang, B. N. Koopmans and C. Pohl, “The 1995 Flood in the Netherlands Monitored from Space—A Multisensor Approach,” International Journal of Remote Sensing, Vol. 16, No. 15, 1995, pp. 2735-2739. doi:10.1080/01431169508956399
[8] D. Amarsaikhan, M. Ganzorig, G. Batbayar, D. Narangerel and S. H. Tumentsetseg, “An Integrated Approach of Optical and SAR Images for Forest Change Study,” Asian Journal of Geoinformatics, Vol. 3, 2004, pp. 27-33.
[9] D. Amarsaikhan, M. Ganzorig, M. Saandar, H. H. Blotevogel, E. Egshiglen, R. Gantuya, B. Nergui and D. Enkhjargal, “Comparison of Multisource Image Fusion Methods and Land Cover Classification,” International Journal of Remote Sensing, Vol. 33, No. 8, 2012, pp. 2532-2550. doi:10.1080/01431161.2011.616552
[10] D. Amarsaikhan, M. Ganzorig, P. Ache and H. H. Blotevogel, “The Integrated Use of Optical and InSAR Data for Urban Land Cover Mapping,” International Journal of Remote Sensing, Vol. 28, No. 6, 2007, pp. 1161-1171. doi:10.1080/01431160600784267
[11] J. A. Benediktsson, J. R. Sveinsson, P. M. Atkinson and A. Tatnali, “Feature Extraction for Multisource Data Classification with Artificial Neural Networks, “International Journal of Remote Sensing, Vol. 18, No. 4, 1997, pp. 727-740. doi:10.1080/014311697218728
[12] S. L. Hegarat-Mascle, A. Quesney, D. Vidal-Madjar, O. Taconet, M. Normand and S. Loumagne, “Land Cover Discrimination from Mutitemporal ERS Imagesand Multispectral Landsat Images: A Study Case in an Agricultural Area in France,” International Journal of Remote Sensing, Vol. 21, No. 3, 2000, pp. 435-456. doi:10.1080/014311600210678
[13] S. B. Serpico and F. Roli, “Classification of Multisensor Remote Sensing Images Bystructural Neural Networks,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 3, 1995, pp. 562-578. doi:10.1109/36.387573
[14] H. Taubenbock, T. Esch, A. Felbier, M. Wiesner, A. Roth and S. Dech, “Monitoring Urbanization in Mega Cities from Space,” Remote Sensing of Environment, Vol. 117, 2012, pp. 162-176. doi:10.1016/j.rse.2011.09.015
[15] Z. Wu, L. Yi and Y. Zhang, “Uncertainty Analysis of Object Location in Multisource Remote Sensing Imagery,” International Journal of Remote Sensing, Vol. 30, No. 20, 2009, pp. 5473-5487. doi:10.1080/01431160903130945
[16] S. E. Franklin, D. R. Peddle, J. A. Dechka and G. B. Stenhouse, “Evidential Reasoning with Landsat TM, DEM and GIS Data for Land Cover Classification in Support of Grizzly Bear Habitat Mapping,” International Journal of Remote Sensing, Vol. 23, No. 21, 2002, pp. 4633-4652. doi:10.1080/01431160110113971
[17] A. H. S. Solberg, T. Taxt and A. K. Jain, “A Markov Random Field Model for Classification of Multisource Satellite Imagery,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 1, 1996, pp. 100-112. doi:10.1109/36.481897
[18] X. Cao, J. Chen, H. Imura and O. Higashi, “A SVMBased Method to Extract Urban Areas from DMSP-OLS and SPOT VGT Data,” Remote Sensing of Environment, Vol. 113, No. 10, 2009, pp. 2205-2209. doi:10.1016/j.rse.2009.06.001
[19] M. Serkan, N. Musaoglu, H. Kirkici and C. Ormeci, “Edge and Fine Detail Preservation in SAR Images through Speckle Reduction with an Adaptive Mean Filter,” International Journal of Remote Sensing, Vol. 29, No. 23, 2008, pp. 6727-6738. doi:10.1080/01431160802029644
[20] Y. Ling, M. Ehlers, E. L. Usery and M. Madden, “Effects of Spatial Resolution Ratio in Image Fusion,” International Journal of Remote Sensing, Vol. 29, No. 7, 2008, pp. 2157-2167. doi:10.1080/01431160701408345
[21] C. Pohl and J. L. Van Genderen, “Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications,” International Journal of Remote Sensing, Vol. 19, No. 5, 1998, pp. 823-854. doi:10.1080/014311698215748
[22] M. A. Abidi and R. C. Gonzalez, “Data Fusion in Robotics and Machine Intelligence,” Academic Press, New York, 1992.
[23] M. Seetha, B. L. Malleswari, I. V. MuraliKrishna and B. L. Deekshatulu, “Image Fusion—A Performance Assessment,” Journal of Geomatics, Vol. 1, No. 1, 2007, pp. 33-39.
[24] J. Vrabel, “Multispectral Imagery Band Sharpening Study,” Photogrammetric Engineering and Remote Sensing, Vol. 62, No. 9, 1996, pp. 1075-1083.
[25] ERDAS, “Field Guide,” 5th Edition, ERDAS, Inc., Atlanta, 1999.
[26] ENVI, “ENVI User’s Guide,” Research Systems, Inc., Norwalk, 1999.
[27] P. M. Mather and M. Koh, “Computer Processing of Remotely-Sensed Images: An Introduction,” 4th Edition, Wiley-Blackwell, New York, 2011.
[28] D. Lu and Q. Weng, “A Survey of Image Classification Methods and Techniques for Improving Classification Performance,” International Journal of Remote Sensing, Vol. 28, No. 5, 2007, pp. 823-870. doi:10.1080/01431160600746456
[29] D. Amarsaikhan, H. H. Blotevogel, J. L. van Genderen, M. Ganzorig, R. Gantuya and B. Nergui, “Fusing High Resolution TerraSAR and Quickbird Images for Urban Land Cover Study in Mongolia,” International Journal of Image and Data Fusion, Vol. 1, No. 1, 2010, pp. 83-97. doi:10.1080/19479830903562041

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.