Abstract
Fine land cover classification in an open pit mining area (LCCOM) is essential in analyzing the terrestrial environment. However, researchers have been focusing on obtaining coarse LCCOM while using high spatial resolution remote sensing data and machine learning algorithms. Although support vector machines (SVM) have been successfully used in the remote sensing community, achieving a high classification accuracy of fine LCCOM using SVM remains difficult because of two factors. One is the lack of significant features for efficiently describing unique terrestrial characteristics of open pit mining areas and another is the lack of an optimized strategy to obtain suitable SVM parameters. This study attempted to address these two issues. Firstly, a novel carbonate index that was based on WorldView-3 was proposed and introduced into the used feature set. Additionally, three optimization methods—genetic algorithm (GA), k-fold cross validation (CV), and particle swarm optimization (PSO)—were used for obtaining the optimization parameters of SVM. The results show that the carbonate index was effective for distinguishing the dumping ground from other open pit mining lands. Furthermore, the three optimization methods could significantly increase the overall classification accuracy (OA) of the fine LCCOM by 8.40%. CV significantly outperformed GA and PSO, and GA performed slightly better than PSO. CV was more suitable for most of the fine land cover types of crop land, and PSO for road and open pit mining lands. The results of an independent test set revealed that the optimized SVM models achieved significant improvements, with an average of 8.29%. Overall, the proposed strategy was effective for fine LCCOM.
Highlights
Land degradation has been increasingly recognized as one of the most destructive impacts on the terrestrial environmental during the last century [1,2]
(1) This study focused on the fine classification of land covers, which is more difficult than that of coarse land covers [6,9]. (2) This study utilized more spectral information, soil adjusted vegetation index, and the proposed Carbonate index (CI), but not topographic variables and the standard deviation filters, which were the top two important feature sets in the previous studies [6]
support vector machines (SVM) models with three parameter optimization methods were investigated for improving the higher accuracy of fine LCCOM based on WV-3 images
Summary
Land degradation has been increasingly recognized as one of the most destructive impacts on the terrestrial environmental during the last century [1,2]. Some researchers have revealed the important effect of open pit mining on local land degradation [1,2,3,4,5]. Land covers in complex open pit mining landscapes are being increasingly used as key datasets for global and local land degradation and development studies [6,7,8,9,10,11,12]. High resolution satellite imagery and machine learning algorithms (MLAs) have been applied to land cover classification in open pit mining areas [6,9,12,13,14]. MLAs can generally accept various features sets [10], which have proven to be valuable in open pit mining areas classification.
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