Abstract
As the ecological problems caused by mine development become increasingly prominent, the conflict between mining activity and environmental protection is gradually intensifying. There is an urgent problem regarding how to effectively monitor mineral exploitation activities. In order to automatic identify and dynamically monitor open-pit mines of Hubei Province, an open-pit mine extraction model based on Improved Mask R-CNN (Region Convolutional Neural Network) and Transfer learning (IMRT) is proposed, a set of multi-source open-pit mine sample databases consisting of Gaofen-1, Gaofen-2 and Google Earth satellite images with a resolution of two meters is constructed, and an automatic batch production process of open-pit mine targets is designed. In this paper, pixel-based evaluation indexes and object-based evaluation indexes are used to compare the recognition effect of IMRT, faster R-CNN, Maximum Likelihood (MLE) and Support Vector Machine (SVM). The IMRT model has the best performance in Pixel Accuracy (PA), Kappa and MissingAlarm, with values of 0.9718, 0.8251 and 0.0862, respectively, which shows that the IMRT model has a better effect on open-pit mine automatic identification, and the results are also used as evaluation units of the environmental damages of the mines. The evaluation results show that level Ⅰ (serious) land occupation and destruction of key mining areas account for 34.62%, and 36.2% of topographical landscape damage approached level I. This study has great practical significance in terms of realizing the coordinated development of mines and ecological environments.
Highlights
With the development of remote sensing technology, target recognition has been widely used
The extraction methods based on pixels mainly include Maximum Likelihood (MLE) classification, Decision Tree (DT) classification and Support Vector Machine (SVM) classification [11,12], which are methods that produce a lot of information redundancy in the extraction process
The traditional extraction manual methods are greatly affected by subjective factors, and they are not universal enough to satisfy the demand of multi-source remote sensing image extraction in open-pit mines
Summary
With the development of remote sensing technology, target recognition has been widely used. As a research hotspot in the field of remote sensing image processing, target identification can effectively identify and dynamically monitor through the mine’s roughness texture and radiation intensity so as to guide environmental protection of the mine, as well as management and ecological restoration [5,6]. The identification and monitoring of open-pit mines mainly adopt traditional extraction manual methods [7,8]. The traditional recognition of remote sensing information in open-pit mines can be divided into pixel-based and object-oriented methods. The traditional extraction manual methods are greatly affected by subjective factors, and they are not universal enough to satisfy the demand of multi-source remote sensing image extraction in open-pit mines
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