浏览全部资源
扫码关注微信
Received：20 January 2020，
Published：07 September 2022
移动端阅览
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。
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.
Bi R T , Bai Z K , Li H and Li W X . 2008 . Land use changes in opencast mine based on RS and GIS technology . Transactions of the CSAE , 24 ( 12 ): 201 - 204
毕如田 , 白中科 , 李华 , 李卫祥 . 2008 . 基于RS和GIS技术的露天矿区土地利用变化分析 . 农业工程学报 , 24 ( 12 ): 201 - 204 [ DOI: 10.3321/j.issn:1002-6819.2008.12.042 http://dx.doi.org/10.3321/j.issn:1002-6819.2008.12.042 ]
Chen Z . 2015 . Multi-label scene classification using convolutional neural network . Ji’nan : Shandong University
陈智 . 2015 . 基于卷积神经网络的多标签场景分类 . 济南 : 山东大学
Cheng L . 2017 . Application of object-oriented combined SVM in information extraction of open-pit mine . Xining : Qinghai University
程璐 . 2017 . 面向对象结合支持向量机(SVM)在露天矿区信息提取中的应用研究 . 西宁 : 青海大学
Dogo E M , Afolabi O J , Nwulu N I , Twala B and Aigbavboa C O . 2018 . A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks // Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems . Institute of Electrical and Electronics Engineers Inc. , United States [ DOI: 10.1109/CTEMS.2018.8769211 http://dx.doi.org/10.1109/CTEMS.2018.8769211 ]
Feng Q L ， Chen B A ， Li G Q ， Yao X C ， Gao B B and Zhang L C . 2022 . A review for sample datasets of remote sensing imagery . National Remote Sensing Bulletin , 26 ( 4 ): 589 - 605
冯权泷 , 陈泊安 , 李国庆 , 姚晓闯 , 高秉博 , 张连翀 . 2022 . 遥感影像样本数据集研究综述 . 遥感学报 , 26 ( 4 ): 589 - 605 [ DOI: 10.11834/jrs.20221162 http://dx.doi.org/10.11834/jrs.20221162 ］
He K , Zhang X , Ren S and Sun J . 2016 . Deep residual learning for image recognition // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . IEEE : 770 - 778 [ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Hu Z Q and Xie H Q . 2005 . Study on land use/cover change of coal mining area based on remote sensing images . Journal of China Coal Society , 30 ( 1 ): 44 - 48
胡振琪 , 谢宏全 . 2005 . 基于遥感图像的煤矿区土地利用/覆盖变化 . 煤炭学报 , 30 ( 1 ): 44 - 48 [ DOI: 10.3321/j.issn:0253-9993.2005.01.010 http://dx.doi.org/10.3321/j.issn:0253-9993.2005.01.010 ]
Ioffe S and Szegedy C . 2015 . Batch normalization: accelerating deep network training by reducing internal covariate shift // Proceedings of the 32nd International Conference on Machine Learning , PMLR 37 : 448 - 456 [ https://dl.acm.org/doi/10.5555/3045118.3045167 https://dl.acm.org/doi/10.5555/3045118.3045167 ]
Jiang X Y . 2017 . Scene Recognition Based on Deep Learning . Nanjing : Nanjing University of Information Science & Technology
姜啸远 . 2017 . 基于深度学习的场景识别研究 . 南京 : 南京信息工程大学
Krizhevsky A , Sutskever I and Hinton G E . 2012 . ImageNet classification with deep convolutional neural networks . Advances in Neural Information Processing Systems , 25 ( 2 ): 1097 - 1105 [ DOI: 10.1145/3065386 http://dx.doi.org/10.1145/3065386 ]
Kurata G , Xiang B and Zhou B . 2016 . Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence // Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics [ DOI: 10.18653/v1/n16-1063 http://dx.doi.org/10.18653/v1/n16-1063 ]
Ma X . 2011 . Ecological Risk Assessment of Vulnerable Mine Area — Taking Shengli East No.2 Open-pit as an Example . Beijing : China University of Geosciences (Beijing)
马萧 . 2011 . 脆弱性矿区生态风险评价——以胜利东二号露天矿为例 . 北京 : 中国地质大学(北京)
Qian X L , Li J , Cheng G , Yao X W , Zhao S N , Chen Y B and Jiang L Y . 2018 . Evaluation of the effect of feature extraction strategy on the performance of high-resolution remote sensing image scene classification . Journal of Remote Sensing , 22 ( 5 ): 758 - 776
钱晓亮 , 李佳 , 程塨 , 姚西文 , 赵素娜 , 陈宜滨 , 姜利英 . 2018 . 特征提取策略对高分辨率遥感图像场景分类性能影响的评估 . 遥感学报 , 22 ( 5 ): 758 - 776 [ DOI: 10.11834/jrs.20188015 http://dx.doi.org/10.11834/jrs.20188015 ]
Shahriyar S A , Alam K M R , Roy S S and Morimoto Y . 2018 . An approach for multi label image classification using single label convolutional neural network // 21st International Conference of Computer and Information Technology . Institute of Electrical and Electronics Engineers Inc. , United States [ DOI: 10.1109/ICCITECHN.2018.8631970 http://dx.doi.org/10.1109/ICCITECHN.2018.8631970 ]
Simonyan K and Zisserman A . 2015 . Very deep convolutional networks for large-scale image recognition // 3rd International Conference on Learning Representations [ https://arxiv.org/abs/1409.1556 https://arxiv.org/abs/1409.1556 ]
Song R Z , Zheng H Y , Wang D C , Shang Z , Wang Y J , Zhang C Y , Li Y . 2022 . Classification of features in open-pit mining areas based on deep learning and high-resolution remote sensing images . China Mining Magazine , 31 ( 7 ): 102 - 111
宋仁忠 , 郑慧玉 , 王党朝 , 尚志 , 王兴娟 , 张成业 , 李军 . 2022 . 基于深度学习和高分辨率遥感影像的露天矿地物分类方法 . 中国矿业 , 31 ( 7 ): 102 - 111 [ DOI: 10.12075/j.issn.1004-4051.2022.07.010 http://dx.doi.org/10.12075/j.issn.1004-4051.2022.07.010 ]
Song Y T , Jiang D , Huang Y H and Wan H W . 2016 . Research on the priority of the land use types extraction of opencast mine area based on object-oriented classification . Remote Sensing Technology Application , 31 ( 3 ): 572 - 579
宋亚婷 , 江东 , 黄耀欢 , 万华伟 . 2016 . 基于面向对象方法的露天煤矿用地类型提取优先级分析 . 遥感技术与应用 , 31 ( 3 ): 572 - 579 [ DOI: 10.11873/j.issn.1004-0323.2016.3.0572 http://dx.doi.org/10.11873/j.issn.1004-0323.2016.3.0572 ]
Szegedy C , Liu W , Jia Y Q , Sermanet P , Reed S , Anguelov D , Erhan D , Vanhoucke V and Rabinovich A . 2015 . Going deeper with convolutions // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . IEEE Computer Society [ DOI: 10.1109/CVPR.2015.7298594 http://dx.doi.org/10.1109/CVPR.2015.7298594 ]
Szegedy C , Vanhoucke V , Ioffe S , Shlens J and Wojna Z . 2016 . Rethinking the inception architecture for computer vision // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . IEEE Computer Society [ DOI: 10.1109/CVPR.2016.308 http://dx.doi.org/10.1109/CVPR.2016.308 ]
Tsoumakas G and Katakis I . 2007 . Multi-label classification: an overview . International Journal of Data Warehousing and Mining , 3 ( 3 ): 1 - 13 [ DOI: 10.4018/jdwm.2007070101 http://dx.doi.org/10.4018/jdwm.2007070101 ]
Williams C K I . 2021 . The effect of class imbalance on precision-recall curves . Neural Computation , 33 ( 4 ): 853 - 857 [ DOI: 10.1162/neco_a_01362 http://dx.doi.org/10.1162/neco_a_01362 ]
Xu S H , Mu X D , Zhao P and Ma J . 2016 . Scene classification of remote sensing image based on multi-scale feature and deep neural network . Acta Geodaetica et Cartographica Sinica , 45 ( 7 ): 834 - 840
许夙晖 , 慕晓冬 , 赵鹏 , 马骥 . 2016 . 利用多尺度特征与深度网络对遥感影像进行场景分类 . 测绘学报 , 45 ( 7 ): 834 - 840 [ DOI: 10.11947/j.AGCS.2016.20150623 http://dx.doi.org/10.11947/j.AGCS.2016.20150623 ]
Yang P , Cai Q Q , Sun H and Sun L H . 2018 . Indoor scene recognition based on convolutional neural network . Journal of Zhengzhou University (Natural Science Edition) , 50 ( 3 ): 73 - 77
杨鹏 , 蔡青青 , 孙昊 , 孙丽红 . 2018 . 基于卷积神经网络的室内场景识别 . 郑州大学学报(理学版) , 50 ( 3 ): 73 - 77 [ DOI: 10.13705/j.issn.1671-6841.2017260 http://dx.doi.org/10.13705/j.issn.1671-6841.2017260 ]
Zhang C Y , Li F Y , Li J , Xing J H , Yang J Z , Guo J T and Du S H . 2022 . Recognition of land use on open-pit coal mining area based on DeepLabv3+ and GF-2 high-resolution images . Coal Geology & Exploration , 50 ( 6 ): 94 - 103
张成业 , 李飞跃 , 李军 , 邢江河 , 杨金中 , 郭俊廷 , 杜守航 . 2022 . 基于DeepLabv3+与GF-2高分辨率影像的露天煤矿区土地利用分类 . 煤田地质与勘探 , 50 ( 6 ): 94 - 103 [ DOI: 10.12363/issn.1001-1986.22.01.0029 http://dx.doi.org/10.12363/issn.1001-1986.22.01.0029 ]
Zhang F J . 2019 . Research on deep learning extraction method in open mining area based on multi-source remote sensing images . Hefei : Anhui University
张峰极 . 2019 . 多源遥感影像露天开采区深度学习提取方法研究 . 合肥 : 安徽大学
Zhang K , Hei B Q , Zhou Z and Li S Y . 2018 . CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification . Journal of Remote Sensing , 22 ( 1 ): 87 - 96
张康 , 黑保琴 , 周壮 , 李盛阳 . 2018 . 变异系数降维的CNN高光谱遥感图像分类 . 遥感学报 , 22 ( 1 ): 87 - 96 [ DOI: 10.11834/jrs.20187075 http://dx.doi.org/10.11834/jrs.20187075 ]
Zhang K X . 2018 . Research and demonstration application on interpretation sign system and information extraction method in open-pit mine . Wuhan : China University of Geosciences(Wuhan)
张凯翔 . 2018 . 露天矿地质环境解译标志体系和信息提取方法的研究及示范应用 . 武汉 : 中国地质大学(武汉)
Zhang M and Zhou Z . 2014 . A review on multi-label learning algorithms . IEEE Transactions on Knowledge and Data Engineering , 26 ( 8 ): 1819 - 1837 [ DOI: 10.1109/TKDE.2013.39 http://dx.doi.org/10.1109/TKDE.2013.39 ]
Zhu Z J . 2010 . Study on mine area information extraction based on object-oriented high-resolution remote sensing image classification and its application . beijing : China University of Geosciences (Beijing)
祝振江 . 2010 . 基于面向对象分类法的高分辨率遥感影像矿山信息提取应用研究 . 北京 : 中国地质大学(北京)
Zuo Z , Wang G , Shuai B , Zhao L F , Yang Q Q and Jiang X D . 2014 . Learning discriminative and shareable features for scene classification // European Conference on Computer Vision . Springer : 552 - 568 [ DOI: 10.1007/978-3-319-10590-1_36 http://dx.doi.org/10.1007/978-3-319-10590-1_36 ]
相关作者
相关机构
京公网安备11010802024621