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Journal of Applied Remote Sensing

Integrated visual vocabulary in latent Dirichlet allocation–based scene classification for IKONOS image
Author(s): Retno Kusumaningrum; Hong Wei; Ruli Manurung; Aniati Murni
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Paper Abstract

Scene classification based on latent Dirichlet allocation (LDA) is a more general modeling method known as a bag of visual words, in which the construction of a visual vocabulary is a crucial quantization process to ensure success of the classification. A framework is developed using the following new aspects: Gaussian mixture clustering for the quantization process, the use of an integrated visual vocabulary (IVV), which is built as the union of all centroids obtained from the separate quantization process of each class, and the usage of some features, including edge orientation histogram, CIELab color moments, and gray-level co-occurrence matrix (GLCM). The experiments are conducted on IKONOS images with six semantic classes (tree, grassland, residential, commercial/industrial, road, and water). The results show that the use of an IVV increases the overall accuracy (OA) by 11 to 12% and 6% when it is implemented on the selected and all features, respectively. The selected features of CIELab color moments and GLCM provide a better OA than the implementation over CIELab color moment or GLCM as individuals. The latter increases the OA by only ∼2 to 3%. Moreover, the results show that the OA of LDA outperforms the OA of C4.5 and naive Bayes tree by ∼20% .

Paper Details

Date Published: 20 January 2014
PDF: 18 pages
J. Appl. Remote Sens. 8(1) 083690 doi: 10.1117/1.JRS.8.083690
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Retno Kusumaningrum, Diponegoro Univ. (Indonesia)
Hong Wei, The Univ. of Reading (United Kingdom)
Ruli Manurung, Univ. of Indonesia (Indonesia)
Aniati Murni, Uinv. of Indonesia (Indonesia)


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