Skip to main content

Advertisement

SpringerLink
Log in

Open-pit Mining Area Segmentation of Remote Sensing Images Based on DUSegNet

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Remote sensing is an important technical means for monitoring and protecting mineral resources. However, because of the complex surface environment, very few good results have been achieved in the study of automatic open-pit mining area segmentation. Inspired by SegNet, UNet and D-LinkNet, this paper proposes a novel deep convolutional neural network for pixel-level semantic segmentation of optical remote sensing images termed DUSegNet. In this network, the pyramid model and upsampling method of pooling indices, similar to SegNet, are employed in the encoder–decoder architecture. In addition, the convolutional skip connection architecture, similar to UNet, is adopted to connect shallow features to the decoder. Additionally, the serial-parallel model, similar to D-LinkNet; the intensifier constructed by dilated convolution; and the classifier constructed by softmax layers are applied in the process of encoding and decoding. In the practical application stage, we present an effective open-pit mining area segmentation method for entire remote sensing images, which has great significance for practical work, such as environmental impact assessment procedures and mine management. In the experimental stage, we compared the open-pit mining area segmentation effects of SegNet, UNet, DecovNet, and DUSegNet on the same dataset manually collected from GF-2 remote sensing images and verified the advantages of DUSegNet using graphic results and optimal evaluation metrics, such as AP (0.94) and F-score (0.67).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis Machine Intelligence, 39(12), 2481–2495.

    Article  Google Scholar 

  • Bayle, Y., Robine, M., & Hanna, P. (2019). SATIN: A persistent musical database for music information retrieval and a supporting deep learning experiment on song instrumental classification. Multimedia Tools and Applications, 78(3), 2703–2718.

    Article  Google Scholar 

  • Cao, B., Qiu, Z., Zhu, S. L., & Cao, B. (2017). Improvement of BPANN based algorithm for estimating water depth from satellite imagery. Bulletin of Surveying and Mapping, 2017(2), 40–44.

    Google Scholar 

  • Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.

    Article  Google Scholar 

  • Chen, X. J. (2017). Comparison of supervised classification methods based on GF-1 satellite image. Mine Surveying, 45(3), 17–19, 29.

    Google Scholar 

  • Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2017). Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2094–2107.

    Article  Google Scholar 

  • Cheng, G., Yang, C. Y., Yao, X. W., Guo, L., & Han, J. (2018). When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2811–2821.

    Article  Google Scholar 

  • Cheng, L. (2017). Application of object-oriented combined SVM in information extraction of open-pit mine. M.Sc. Dissertation, Qinghai University, Qinghai, China.

  • Gao, C. X., & Sang, N. (2014). Deep learning for object detection in remote sensing image. Bulletin of Surveying and Mapping, 2014(S1), 108–111.

    Google Scholar 

  • Gao, L., Luo, J., Xia, L., Wu, T., Sun, Y., & Liu, H. (2019). Topographic constrained land cover classification in mountain areas using fully convolutional network. International Journal of Remote Sensing, 40(18), 7127–7152.

    Article  Google Scholar 

  • Hossain, M. S., & Muhammad, G. (2019). Emotion recognition using deep learning approach from audio-visual emotional big data. Information Fusion, 49, 69–78.

    Article  Google Scholar 

  • Hu, R., Liu, J., & Zhai, M. (2010). Mineral resources science in China: A roadmap to 2050. Beijing: Science Press.

    Book  Google Scholar 

  • Kumar, P., Nagar, P., Arora, C., & Gupta, A. (2018). U-SegNet: Fully convolutional neural network based automated brain tissue segmentation tool. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 3503–3507).

  • Li, R., Liu, W., Yang, L., Sun, S., Hu, W., & Zhang, F. (2018). DeepUNet: A deep fully convolutional network for pixel-level sea-land segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(11), 3954–3962.

    Article  Google Scholar 

  • Liu, J., Du, M., & Mao, Z. (2017). Scale computation on high spatial resolution remotely sensed imagery multi-scale segmentation. International Journal of Remote Sensing, 38(18), 5186–5214.

    Article  Google Scholar 

  • Lu, J., Wang, Y., Zhu, Y., Ji, X., Xing, T., Li, W., et al. (2019). P-Segnet and NP-Segnet: New neural network architectures for cloud recognition of remote sensing images. IEEE Access, 7(1), 87323–87333.

    Article  Google Scholar 

  • Luus, F. P. S., Salmon, B. P., Bergh, F., & Maharaj, B. T. J. (2015). Multiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters, 12(12), 2448–2452.

    Article  Google Scholar 

  • Maxwell, A. E., Strager, M. P., Warner, T. A., Zegre, N. P., & Yuill, C. B. (2014). Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation. GIScicence & Remote Sensing, 51(3), 301–320.

    Article  Google Scholar 

  • Mittal, A., Hooda, R., & Sofat, S. (2018). LF-SegNet: A fully convolutional encoder–decoder network for segmenting lung fields from chest radiographs. Wireless Personal Communications, 101(1), 511–529.

    Article  Google Scholar 

  • Noh, H., Hong, S., Han, B. (2015). Learning deconvolution network for semantic segmentation. In 2015 IEEE international conference on computer vision (ICCV) (pp. 1520–1528).

  • Ouma, Y. O., Ngigi, T. G., & Tateishi, R. (2006). On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation. International Journal of Remote Sensing, 27(1), 73–104.

    Article  Google Scholar 

  • Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222.

    Article  Google Scholar 

  • Peng, D., Zhang, Y., & Guan, H. (2019). End-to-End change detection for high resolution satellite images using improved UNet plus. Remote Sensing, 11(11), 1382.

    Article  Google Scholar 

  • Segata, N., Pasolli, E., Melgani, F., & Blanzieri, E. (2012). Local SVM approaches for fast and accurate classification of remote-sensing images. International Journal of Remote Sensing, 33(19), 6186–6201.

    Article  Google Scholar 

  • Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640–651.

    Article  Google Scholar 

  • Shin, H. C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., et al. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298.

    Article  Google Scholar 

  • Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018, 7068349.

    Google Scholar 

  • Yang, H. C. (2015). Land use remote sensing at mining area and analysis of landscape pattern-a case study of Lianjiang River Basin. M.Sc. Dissertation, Jiangxi University of Science and Technology, Ganzhou, China.

  • Zhang, C., Liu, J., Yu, F., Wan, S., Han, Y., Wang, J., et al. (2018). Segmentation model based on convolutional neural networks for extracting vegetation from Gaofen-2 images. Journal of Applied Remote Sensing, 12(4), 042804.

    Google Scholar 

  • Zhang, M., Hu, X., Zhao, L., Pang, S., & Luo, M. (2017). Translation-aware semantic segmentation via conditional least square generative adversarial networks. Journal of Applied Remote Sensing, 11(4), 042622.

    Google Scholar 

  • Zhao, K., Kang, J., Jung, J., & Sohn, G. (2018). Building extraction from satellite images using Mask R-CNN with building boundary regularization. In 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW) (pp. 242–246).

  • Zhong, Y., Fei, F., & Zhang, L. (2016). Large patch convolutional neural networks for the scene classification of high spatial resolution imagery. Journal of Applied Remote Sensing, 10(2), 025006.

    Article  Google Scholar 

  • Zhou, L., Zhang, C., & Wu, M. (2018). D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In 2018 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW) (pp. 192–196).

Download references

Funding

Funding was provided by Natural Science Foundation of Chongqing, China (Grant Number cstc2019jcyj-msxmX0657).

Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Exogenic Mineralization and Mine Environment, Chongqing Institute of Geology and Mineral Resources, Chongqing, 401120, China

    Hongbin Xie, Yongzhuo Pan, Jinhua Luan, Xue Yang & Yawen Xi

  2. Chongqing Research Center of State Key Laboratory of Coal Resources and Safe Mining, Chongqing, 401120, China

    Hongbin Xie, Yongzhuo Pan, Jinhua Luan, Xue Yang & Yawen Xi

Authors
  1. Hongbin Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongzhuo Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinhua Luan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xue Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yawen Xi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongzhuo Pan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, H., Pan, Y., Luan, J. et al. Open-pit Mining Area Segmentation of Remote Sensing Images Based on DUSegNet. J Indian Soc Remote Sens 49, 1257–1270 (2021). https://doi.org/10.1007/s12524-021-01312-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-021-01312-x

Keywords

Search

Navigation