基于子区域多标签学习的露天煤矿区场景识别

赵银娣; 卫虹宇; 董霁红; 董畅

doi:10.11834/jrs.20210001

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基于子区域多标签学习的露天煤矿区场景识别
Opencast coal mine scene recognition based on sub-region multi-label learning
2022年26卷第9期页码：1849-1858
收稿：2020-01-20，

纸质出版：2022-09-07
DOI： 10.11834/jrs.20210001
稿件说明：

移动端阅览

赵银娣，卫虹宇，董霁红，董畅.2022.基于子区域多标签学习的露天煤矿区场景识别.遥感学报，26（9）： 1849-1858 DOI： 10.11834/jrs.20210001.

Zhao Y D，Wei H Y，Dong J H and Dong C. 2022. Opencast coal mine scene recognition based on sub-region multi-label learning. National Remote Sensing Bulletin， 26（9）：1849-1858 DOI： 10.11834/jrs.20210001.

摘要

露天煤矿开采易对区域生态环境产生不利影响，对其进行高效监管有利于矿区环境保护和可持续发展。随着遥感技术和人工智能的发展，基于高分辨率遥感影像的露天煤矿区场景自动识别成为可能。本文针对单标签学习算法在场景子区域识别中识别率较低的问题，将多标签学习策略和地理学第一定律相结合，提出一种基于子区域多标签学习的露天煤矿区场景识别方法。为了区分露天煤矿区场景与其周边场景，设置了6类矿区标签和7类非矿区标签，对9768张场景子区域图像进行标注，构建多标签数据集，利用该数据集训练基于多标签学习的Inception-v3模型。场景识别时，首先将一幅覆盖研究区的遥感影像划分为相同大小的子区域并进行多标签分类；然后对含有矿区标签的子区域，利用地理学第一定律对其矿区标签的相关性和完整性进行判定，识别出属于露天煤矿区场景的子区域。胜利西露天煤矿区识别实验结果表明：该方法提取的结果最接近真值，显著高于单标签学习的识别精度；其子区域多标签分类F1分数达到0.857，与单标签学习中性能最好的ResNet50模型相比，提高了8个百分点。本文提出的方法能够自动提取子区域内多类标签的有效特征，提高露天煤矿区场景识别的精度，其识别结果可为露天矿区开采管理提供数据支撑。

Abstract

Opencast coal mining activities would lead to negative impacts on regional ecological environment

ensuring efficient monitoring and regulation of mining activities would promote environmental protection and sustainable development. With the development of remote sensing technology and artificial intelligence

there is great potential in automatically detecting opencast coal mine areas from high spatial resolution remote sensing imagery. Aiming at the problem of low recognition rate of scene sub-region recognition via single-label learning algorithm

this paper proposes an opencast coal mine scene recognition method by integrating multi-label learning and the first law of geography. In order to distinguish the opencast coal mine scenes from their surrounding different scenes

six categories of mining labels and seven categories of non-mining labels are set

and 9768 sub-region images are annotated to create a multi-label dataset. The Inception-v3 model is trained using the created dataset to perform multi-label classification. For scene recognition

firstly

the remote sensing image covering the study area is divided into non-overlapping sub-regions of the same size and the multi-label classification is carried out on the divided sub-regions. Then

inspired by the first law of geography

the sub-regions containing the mining labels are assigned to the coal mine scene type or not according to the correlation between the labels and the completeness of the labels. Finally

all the sub-regions judged as the coal mine scene type constitute the opencast coal mine scene recognition result from the high spatial resolution remote sensing image covering the study area. The experimental results show that the recognition result of Shengli west opencast coal mine areas obtained by the proposed method is much closer to the ground truth than the comparative methods based on single-label learning. The F1 score of the proposed method reaches 0.857 in the multi-label classification

with an increase of 8 percentage points compared to the ResNet50 single-label learning method which has the best performance in the compared single-label learning methods. The proposed method can automatically extract the effective features of multiple labels in sub-regions and improve the performance of opencast coal mine scene recognition

its recognition results can provide data support for opencast mining management.

关键词

Keywords

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