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Remote Sens. 2016, 8(12), 1023; doi:10.3390/rs8121023

Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution

1
School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
2
Department of Land Surveying and Geo-Informatics, the Hong Kong Polytechnic University, Hong Hom, Hong Kong 999000, China
3
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland
*
Authors to whom correspondence should be addressed.
Academic Editors: Yuei-An Liou, Chyi-Tyi Lee, Yuriy Kuleshov, Jean-Pierre Barriot, Chung-Ru Ho, Jose Moreno, Richard Gloaguen and Prasad S. Thenkabail
Received: 4 October 2016 / Revised: 4 December 2016 / Accepted: 9 December 2016 / Published: 15 December 2016
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
View Full-Text   |   Download PDF [17819 KB, uploaded 19 December 2016]   |  

Abstract

Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%–18% improvement in overall accuracy. View Full-Text
Keywords: image filter; very high spatial resolution (VHSR) aerial image; multi-scale segmentation; land cover classification image filter; very high spatial resolution (VHSR) aerial image; multi-scale segmentation; land cover 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

Lv, Z.; Shi, W.; Benediktsson, J.A.; Ning, X. Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution. Remote Sens. 2016, 8, 1023.

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