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Remote Sens. 2017, 9(3), 223; doi:10.3390/rs9030223

Improved Class-Specific Codebook with Two-Step Classification for Scene-Level Classification of High Resolution Remote Sensing Images

School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Academic Editors: Lizhe Wang, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 21 December 2016 / Accepted: 25 February 2017 / Published: 2 March 2017

Abstract

With the rapid advances in sensors of remote sensing satellites, a large number of highresolution images (HRIs) can be accessed every day. Land use classification using high-resolution images has become increasingly important as it can help to overcome the problems of haphazard, deteriorating environmental quality, loss of prime agricultural lands, and destruction of important wetlands, and so on. Recently, local feature with bag-of-words (BOW) representation has been successfully applied to land-use scene classification with HRIs. However, the BOW representation ignores information from scene labels, which is critical for scene-level land-use classification. Several algorithms have incorporated information from scene labels into BOW by calculating a class-specific codebook from the universal codebook and coding a testing image with a number of histograms. Those methods for mapping the BOW feature to some inaccurate class-specific codebooks may increase the classification error. To effectively solve this problem, we propose an improved class-specific codebook using kernel collaborative representation based classification (KCRC) combined with SPM approach and SVM classifier to classify the testing image in two steps. This model is robust for categories with similar backgrounds. On the standard Land use and Land Cover image dataset, the improved class-specific codebook achieves an average classification accuracy of 93% and demonstrates superiority over other state-of-the-art scene-level classification methods. View Full-Text
Keywords: scene-level land use classification; Bag-of-words (BOW); improved class-specific codebook; kernel collaborative representative based classification combined with SPM; two-step classification scene-level land use classification; Bag-of-words (BOW); improved class-specific codebook; kernel collaborative representative based classification combined with SPM; two-step classification
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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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MDPI and ACS Style

Yan, L.; Zhu, R.; Mo, N.; Liu, Y. Improved Class-Specific Codebook with Two-Step Classification for Scene-Level Classification of High Resolution Remote Sensing Images. Remote Sens. 2017, 9, 223.

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