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Remote Sens. 2016, 8(7), 555; doi:10.3390/rs8070555

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan 430079, China
2
Electronic Information School, Wuhan University, Wuhan 430072, China
3
Global Navigation Satellite System (GNSS) Research Center, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou and Prasad S. Thenkabail
Received: 21 January 2016 / Revised: 14 June 2016 / Accepted: 22 June 2016 / Published: 30 June 2016
View Full-Text   |   Download PDF [3512 KB, uploaded 30 June 2016]   |  

Abstract

Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy. View Full-Text
Keywords: scene classification; filter banks; feature representation; binary coding; high-resolution remote sensing images scene classification; filter banks; feature representation; binary coding; high-resolution remote sensing images
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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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Hu, F.; Xia, G.-S.; Hu, J.; Zhong, Y.; Xu, K. Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery. Remote Sens. 2016, 8, 555.

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