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Journal of Applied Remote Sensing

Sparse coding-based correlaton model for land-use scene classification in high-resolution remote-sensing images
Author(s): Kunlun Qi; Zhang Xiaochun; Wu Baiyan; Huayi Wu
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Paper Abstract

High-resolution remote-sensing images are increasingly applied in land-use classification problems. Land-use scenes are often very complex and difficult to represent. Subsequently, the recognition of multiple land-cover classes is a continuing research question. We propose a classification framework based on a sparse coding-based correlaton (termed sparse correlaton) model to solve this challenge. Specifically, a general mapping strategy is presented to label visual words and generate sparse coding-based correlograms, which can exploit the spatial co-occurrences of visual words. A compact spatial representation without loss discrimination is achieved through adaptive vector quantization of correlogram in land-use scene classification. Moreover, instead of using K-means for visual word encoding in the original correlaton model, our proposed sparse correlaton model uses sparse coding to achieve lower reconstruction error. Experiments on a public ground truth image dataset of 21 land-use classes demonstrate that our sparse coding-based correlaton method can improve the performance of land-use scene classification and outperform many existing bag-of-visual-words-based methods.

Paper Details

Date Published: 20 June 2016
PDF: 11 pages
J. Appl. Rem. Sens. 10(4) 042005 doi: 10.1117/1.JRS.10.042005
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Kunlun Qi, Wuhan Univ. (China)
Zhang Xiaochun, Wuhan Univ. (China)
Wu Baiyan, Hunan Univ. of Science and Technology (China)
Huayi Wu, Wuhan Univ. (China)

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