Abstract
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.
Highlights
Experiment 1 using feature Combination 1 aimed to analyze the effects of the addition of different types of feature sets for LCC in surface-mined landscapes (LCCSML) to assess the importance of feature sets (Section 3.2)
Since the feature sets had different size, for example vegetation index (VI), principal component bands (PCs), and topographic variables (TVs) had only 1–3 features, the others had 12 features (GLP, mean filters (Mean), standard deviation filters (StDev)) or 60 features (Textures), it is possible that some feature sets resulted in higher accuracy improvements only due to the larger number of features within the set
For Gaussian low-pass filters (GLP), Mean, and StDev, some of their features were selected as members of the feature subset, and the results revealed that those feature sets could provide effective information for the LCCSML
Summary
Land cover datasets are basic components for global change studies and various applications [1,2].Currently, researchers are mainly focusing on land cover classification (LCC) at fine scales [3,4,5] in complex landscapes such as agricultural [6,7,8,9], surface-mined land [10,11,12,13,14], and Mediterranean [15]by using high spatial resolution satellite imagery. Researchers are mainly focusing on land cover classification (LCC) at fine scales [3,4,5] in complex landscapes such as agricultural [6,7,8,9], surface-mined land [10,11,12,13,14], and Mediterranean [15]. There are other landscapes in surface-mined areas, such as agricultural, forest, and cities. They can be considered as complex surface-mined landscape together for LCC. Classification technology based on machine learning algorithms and high spatial resolution imagery has achieved more accurate results for urban environments, precision agriculture, Remote Sens. Classification technology based on machine learning algorithms and high spatial resolution imagery has achieved more accurate results for urban environments, precision agriculture, Remote Sens. 2018, 10, 23; doi:10.3390/rs10010023 www.mdpi.com/journal/remotesensing
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