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Classification of hyperspectral data for land cover mapping : is there any significant improvement?

Lau Alvin, Meng Shin and Hashim, Mazlan (2003) Classification of hyperspectral data for land cover mapping : is there any significant improvement? Geoinformation Science Journal, 3 (1). pp. 74-79. ISSN 1511-9491

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Abstract

This paper highlights the results of classification of an airborne MASTER hyperspectral data for land cover mapping in Redang Island, Malaysia. Two addressed issues in the study are: (1) whether or not hyperspectral would increase classification accuracy over medium spatial resolution (10m) of MASTER data for land cover mapping, and (2) radiometric normalization still required in hyperspectral data Three classification algorithms examined in this study, namely Binary encoding, Spectral Angle Mapper and Linear spectral Unmixing. The topographic-effect normalization was applied to the test site prior data classification. Results of study indicated that Linear Spectral Unmixing classification technique gives the best overall classification accuracy of the hyperspectral data for land cover in the study area. The result of this study also clearly indicated that hyperspectral data could not improve classification accuracy significantly especially when the .mixed pixels are abundant

Item Type:Article
Uncontrolled Keywords:hyperspectral remote sensing, binary encoding, spectral angle mapper, linear spectral unmixing, topographic normalization
Subjects:G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
Divisions:Geoinformation Science And Engineering (Formerly known)
ID Code:12593
Deposited By: Ms Zalinda Shuratman
Deposited On:14 Jun 2011 08:12
Last Modified:14 Jun 2011 08:12

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