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Open AccessArticle

Assessing Different Feature Sets’ Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery

by Weitao Chen 1,2, Xianju Li 1,2,*, Haixia He 3 and Lizhe Wang 1,2,*
1
Faculty of Computer Science and Geological Survey of CUG, China University of Geosciences, Wuhan 430074, China
2
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
3
National Disaster Reduction Center of China, Beijing 100124, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 23; https://doi.org/10.3390/rs10010023
Received: 6 November 2017 / Revised: 20 December 2017 / Accepted: 22 December 2017 / Published: 23 December 2017
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
Land cover classification (LCC) in complex surface-mined landscapes has become very important for understanding the influence of mining activities on the regional geo-environment. There are three characteristics of complex surface-mined areas limiting LCC: significant three-dimensional terrain, strong temporal-spatial variability of surface cover, and spectral-spatial homogeneity. Thus, determining effective feature sets are very important as input dataset to improve detailed extent of classification schemes and classification accuracy. In this study, data such as various feature sets derived from ZiYuan-3 stereo satellite imagery, a feature subset resulting from a feature selection (FS) procedure, training data polygons, and test sample sets were firstly obtained; then, feature sets’ effects on classification accuracy was assessed based on different feature set combination schemes, a FS procedure, and random forest algorithm. The following conclusions were drawn. (1) The importance of feature set could be divided into three grades: the vegetation index (VI), principal component bands (PCs), mean filters (Mean), standard deviation filters (StDev), texture measures (Textures), and topographic variables (TVs) were important; the Gaussian low-pass filters (GLP) was just positive; and none were useless. The descending order of their importance was TVs, StDev, Textures, Mean, PCs, VI, and GLP. (2) TVs and StDev both significantly outperformed VI, PCs, GLP, and Mean; Mean outperformed GLP; all other pairs of feature sets had no difference. In general, the study assessed different feature sets’ effects on LCC in complex surface-mined landscapes. View Full-Text
Keywords: remote sensing; land cover classification; importance of feature set; complex landscape; surface mining remote sensing; land cover classification; importance of feature set; complex landscape; surface mining
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MDPI and ACS Style

Chen, W.; Li, X.; He, H.; Wang, L. Assessing Different Feature Sets’ Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery. Remote Sens. 2018, 10, 23.

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