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Article

A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery

1
College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China
2
Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
3
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
4
Xinjiang Institute of Ecology and Geography, CAS and the CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
5
Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 862; https://doi.org/10.3390/rs12050862
Received: 29 January 2020 / Revised: 2 March 2020 / Accepted: 5 March 2020 / Published: 7 March 2020
(This article belongs to the Special Issue Multi-Modality Data Classification: Algorithms and Applications)
In this article, a novel feature selection-based multi-scale superpixel-based guided filter (FS-MSGF) method for classification of very-high-resolution (VHR) remotely sensed imagery is proposed. Improved from the original guided filter (GF) algorithm used in the classification, the guidance image in the proposed approach is constructed based on the superpixel-level segmentation. By taking into account the object boundaries and the inner-homogeneity, the superpixel-level guidance image leads to the geometrical information of land-cover objects in VHR images being better depicted. High-dimensional multi-scale guided filter (MSGF) features are then generated, where the multi-scale information of those land-cover classes is better modelled. In addition, for improving the computational efficiency without the loss of accuracy, a subset of those MSGF features is then automatically selected by using an unsupervised feature selection method, which contains the most distinctive information in all constructed MSGF features. Quantitative and qualitative classification results obtained on two QuickBird remotely sensed imagery datasets covering the Zurich urban scene are provided and analyzed, which demonstrate that the proposed methods outperform the state-of-the-art reference techniques in terms of higher classification accuracies and higher computational efficiency. View Full-Text
Keywords: guided filter (GF); superpixel segmentation; multi-scale features; feature selection; classification; VHR remote sensing images guided filter (GF); superpixel segmentation; multi-scale features; feature selection; classification; VHR remote sensing images
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MDPI and ACS Style

Liu, S.; Hu, Q.; Tong, X.; Xia, J.; Du, Q.; Samat, A.; Ma, X. A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery. Remote Sens. 2020, 12, 862. https://doi.org/10.3390/rs12050862

AMA Style

Liu S, Hu Q, Tong X, Xia J, Du Q, Samat A, Ma X. A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery. Remote Sensing. 2020; 12(5):862. https://doi.org/10.3390/rs12050862

Chicago/Turabian Style

Liu, Sicong, Qing Hu, Xiaohua Tong, Junshi Xia, Qian Du, Alim Samat, and Xiaolong Ma. 2020. "A Multi-Scale Superpixel-Guided Filter Feature Extraction and Selection Approach for Classification of Very-High-Resolution Remotely Sensed Imagery" Remote Sensing 12, no. 5: 862. https://doi.org/10.3390/rs12050862

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