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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

has been cited by the following article:

• TITLE: Advanced Classification of Lands at TM and Envisat Images of Mongolia

  AUTHORS: V. Battsengel, D. Amarsaikhan, Ts. Bat-Erdene, E. Egshiglen, A. Munkh-Erdene, M. Ganzorig

  KEYWORDS: Image Fusion; Mahalanobis Distance; Refined Method; Classification

  JOURNAL NAME: Advances in Remote Sensing, Vol.2 No.2, June 11, 2013

  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.