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ISPRS Int. J. Geo-Inf. 2017, 6(6), 175; doi:10.3390/ijgi6060175

Generalized Aggregation of Sparse Coded Multi-Spectra for Satellite Scene Classification

1
Graduate School of Science and Technology for Innovation, Yamaguchi University, 1677-1 Yoshida, Yamaguchi City, Yamaguchi 753-8511, Japan
2
College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Japan
Current address: Yamaguchi University, 1677-1 Yoshida, Yamaguchi City, Yamaguchi 753-8511, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Ozgun Akcay and Wolfgang Kainz
Received: 25 April 2017 / Revised: 10 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
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Abstract

Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spectral bands than a general image, thereby permitting the multi-spectral analysis of different land materials and promoting low-resolution satellite scene recognition. This study advocates multi-spectral analysis and explores the middle-level statistics of spectral information for satellite scene representation instead of using spatial analysis. This approach is widely utilized in general image and natural scene classification and achieved promising recognition performance for different applications. The proposed multi-spectral analysis firstly learns the multi-spectral prototypes (codebook) for representing any pixel-wise spectral data, and then, based on the learned codebook, a sparse coded spectral vector can be obtained with machine learning techniques. Furthermore, in order to combine the set of coded spectral vectors in a satellite scene image, we propose a hybrid aggregation (pooling) approach, instead of conventional averaging and max pooling, which includes the benefits of the two existing methods, but avoids extremely noisy coded values. Experiments on three satellite datasets validated that the performance of our proposed approach is very impressive compared with the state-of-the-art methods for satellite scene classification. View Full-Text
Keywords: multi-spectral analysis; remote sensing images; sparse coding; generalized aggregation; scene recognition multi-spectral analysis; remote sensing images; sparse coding; generalized aggregation; scene recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Han, X.-H.; Chen, Y.-W. Generalized Aggregation of Sparse Coded Multi-Spectra for Satellite Scene Classification. ISPRS Int. J. Geo-Inf. 2017, 6, 175.

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