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Remote Sens. 2017, 9(9), 917; doi:10.3390/rs9090917

A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification

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1,2,* , 1,2
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and
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1
National Engineering Research Center of Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China
2
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
3
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan 430079, China
4
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 25 May 2017 / Revised: 16 August 2017 / Accepted: 30 August 2017 / Published: 2 September 2017
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
View Full-Text   |   Download PDF [2444 KB, uploaded 2 September 2017]   |  

Abstract

Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and challenging. This article presents a Multiscale Deeply Described Correlatons (MDDC)-based algorithm which incorporates appearance and spatial information jointly at multiple scales for land-use scene classification to tackle these problems. Specifically, we introduce a convolutional neural network to learn and characterize the dense convolutional descriptors at different scales. The resulting multiscale descriptors are used to generate visual words by a general mapping strategy and produce multiscale correlograms of visual words. Then, an adaptive vector quantization of multiscale correlograms, termed multiscale correlatons, are applied to encode the spatial arrangement of visual words at different scales. Experiments with two publicly available land-use scene datasets demonstrate that our MDDC model is discriminative for efficient representation of land-use scene images, and achieves competitive classification results with state-of-the-art methods. View Full-Text
Keywords: convolutional neural network; spatial information; multiple scales; feature representation convolutional neural network; spatial information; multiple scales; feature representation
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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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Qi, K.; Yang, C.; Guan, Q.; Wu, H.; Gong, J. A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification. Remote Sens. 2017, 9, 917.

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