基于改进Faster-RCNN的露天煤矿开采区遥感识别方法

doi:10.12068/j.issn.1005-3026.2023.12.012

东北大学学报（自然科学版） ›› 2023, Vol. 44 ›› Issue (12): 1759-1768.DOI: 10.12068/j.issn.1005-3026.2023.12.012

基于改进Faster-RCNN的露天煤矿开采区遥感识别方法

包妮沙¹，韩子松¹，于嘉欣²，韦丽红³

( 1. 东北大学资源与土木工程学院，辽宁沈阳110819； 2. 长光卫星技术有限公司，吉林长春130000； 3. 呼伦贝尔学院，内蒙古呼伦贝尔021008)

发布日期:2024-01-30
通讯作者: 包妮沙
作者简介:包妮沙(1985-)，女，内蒙古呼伦贝尔人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(52074063，U1903216)；内蒙古自治区高等学校科学技术研究自然科学项目(NJZY22278).

Remote Sensing Identification Method for Open-Pit Coal Mining Area Based on Improved Faster-RCNN

BAO Ni-sha¹， HAN Zi-song¹， YU Jia-xin²， WEI Li-hong ³

1. School of Resources & Civil Engineering， Northeastern University， Shenyang 110819， China; 2. Changguang Satellite Technology Co.， Ltd， Changchun 130000， China; 3. Hulun Buir College， Hulun Buir 021008， China.

Published:2024-01-30
Contact: BAO Ni-sha
About author:-
Supported by:
-

摘要/Abstract

摘要： 利用卫星遥感技术融合深度学习算法，可以快速、动态、高效识别露天煤矿开采区，以我国和其他煤炭资源大国的典型露天煤矿开采区为研究对象，基于高分二号多光谱遥感影像，制作数据集及标签，构建基于卷积神经网络(convolutional neural networks，CNN)的深度学习目标检测算法.通过加入特征金字塔网络，充分挖掘开采区及背景区的低分辨率语义信息和高分辨率纹理信息，实现快速卷积神经网络的深度学习目标检测算法模型的改进及参数优化.结果表明改进后的模型平均检测精度提高到98.48%，总体识别精度达到96.7%，有效提高了复杂背景下的多尺度、多类型露天开采目标的识别精度，为全球煤炭资源大国能源合作、生态环境保护及我国矿产资源的合理利用和修复提供了科学、精准手段.

关键词: 高分影像；露天开采区；目标检测；特征金字塔

Abstract: Using satellite remote sensing technology coupled with deep learning algorithms could characterize open-pit coal mining area dynamically and efficiently. This study focused on typical open-pit coal mining areas from China and other major coal-resource countries as research objects. Based on GF-2 multi-spectral remote sensing images， data sets and labels are produced to construct faster-regions with convolutional neural networks(CNN) features target recognition model. Low-resolution semantic layer and high-resolution texture information of mining area and background area were fully mined by incorporating the feature Pyramid network， which could improve faster-regions with CNN features model by optimizing parameters. The results showed that the average detection accuracy was improved to 98.48%， and the overall recognition accuracy reached 96.7%. The improved faster-regions with CNN features model efficiently increases the identification accuracy of multi-scale and multi-type open-pit mining targets in complex backgrounds. These findings can provide a scientific and accurate technical way for global energy cooperation， environmental protection， and mineral resources utilization in China.

Key words: domestic high-resolution imagery; open-pit coal mining areas; object detection; feature pyramids

中图分类号:

TP753

包妮沙，韩子松，于嘉欣，韦丽红. 基于改进Faster-RCNN的露天煤矿开采区遥感识别方法[J]. 东北大学学报（自然科学版）, 2023, 44(12): 1759-1768.

BAO Ni-sha， HAN Zi-song， YU Jia-xin， WEI Li-hong. Remote Sensing Identification Method for Open-Pit Coal Mining Area Based on Improved Faster-RCNN[J]. Journal of Northeastern University(Natural Science), 2023, 44(12): 1759-1768.

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基于改进Faster-RCNN的露天煤矿开采区遥感识别方法

Remote Sensing Identification Method for Open-Pit Coal Mining Area Based on Improved Faster-RCNN

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