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REN Shiman, ZHU Jun, FANG Zheng, LI Chuangnong, LIANG Ce, XIE Yakun, LI Weilian, ZHANG Tianyi. Woodland Extraction of SPOT7 Image Based on Multi-scale Attention Mechanism and Edge Constraint[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210251
Citation: REN Shiman, ZHU Jun, FANG Zheng, LI Chuangnong, LIANG Ce, XIE Yakun, LI Weilian, ZHANG Tianyi. Woodland Extraction of SPOT7 Image Based on Multi-scale Attention Mechanism and Edge Constraint[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210251

Woodland Extraction of SPOT7 Image Based on Multi-scale Attention Mechanism and Edge Constraint

doi: 10.13203/j.whugis20210251
Funds:

Project of Department of Natural Resources of Sichuan Province(KJ-2020-4)

  • Received Date: 2021-12-19
    Available Online: 2022-07-26
  • As woodland is an important natural and economic resource of China, it is important to understand the distribution of woodland for the investigation and management of woodland resources. In this paper, we design a woodland extraction method combining multi-scale attention mechanism and edge constraint to tackle the issue of low accuracy and irregular boundaries in traditional forest extraction methods. First, an end-to-end multi-scale attentional neural network model is constructed to fully extract the context features of woodland in remote sensing images, and semantically describe woodland at different scales to achieve high-precision pixel-level expression of woodland. Secondly, the edge constraint rules are constructed to optimize the boundary of the extraction results, to improve the readability of the extraction results. To prove the effectiveness of the proposed method, Santai County, Mianyang City, Sichuan Province, is taken as the experimental area to establish datasets and carry out woodland extraction experiments. The results show that the extraction accuracy of this method is 81.9%, the recall rate is 75.6%, F1 is 78.1%, IoU(Intersection of Union) is 64.2%, and the method in the paper has a better effect in the application of woodland extraction with remote sensing image.
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    [2] Kim C, Hong S. The Characterization of a Forest Cover through Shape and Texture Parameters from QuickBird Imagery[C]. IEEE International Geoscience and Remote Sensing Symposium, 2008, 3:692-695
    [3] Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4):640-651
    [4] Kattenborn T, Leitloff J, Schiefer F, et al. Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 173:24-49
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    [6] Schiefer F, Kattenborn T, Frick A, et al. Mapping Forest Tree Species in High Resolution UAVBased RGB-imagery by Means of Convolutional Neural Networks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170:205-215
    [7] Gui Y, Li W, Wang Y, et al. Woodland Detection Using Most-sure Strategy to Fuse Segmentation Results of Deep Learning[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019:1-4
    [8] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016:770-778
    [9] Ronneberger O, Fischer P, Brox T. U-Net:Convolutional Networks for Biomedical Image Segmentation[J]. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015, 9351:234-241
    [10] Xie S, Girshick R, Dollár P, et al. Aggregated Residual Transformations for Deep Neural Networks[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017:5987-5995
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Woodland Extraction of SPOT7 Image Based on Multi-scale Attention Mechanism and Edge Constraint

doi: 10.13203/j.whugis20210251
Funds:

Project of Department of Natural Resources of Sichuan Province(KJ-2020-4)

Abstract: As woodland is an important natural and economic resource of China, it is important to understand the distribution of woodland for the investigation and management of woodland resources. In this paper, we design a woodland extraction method combining multi-scale attention mechanism and edge constraint to tackle the issue of low accuracy and irregular boundaries in traditional forest extraction methods. First, an end-to-end multi-scale attentional neural network model is constructed to fully extract the context features of woodland in remote sensing images, and semantically describe woodland at different scales to achieve high-precision pixel-level expression of woodland. Secondly, the edge constraint rules are constructed to optimize the boundary of the extraction results, to improve the readability of the extraction results. To prove the effectiveness of the proposed method, Santai County, Mianyang City, Sichuan Province, is taken as the experimental area to establish datasets and carry out woodland extraction experiments. The results show that the extraction accuracy of this method is 81.9%, the recall rate is 75.6%, F1 is 78.1%, IoU(Intersection of Union) is 64.2%, and the method in the paper has a better effect in the application of woodland extraction with remote sensing image.

REN Shiman, ZHU Jun, FANG Zheng, LI Chuangnong, LIANG Ce, XIE Yakun, LI Weilian, ZHANG Tianyi. Woodland Extraction of SPOT7 Image Based on Multi-scale Attention Mechanism and Edge Constraint[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210251
Citation: REN Shiman, ZHU Jun, FANG Zheng, LI Chuangnong, LIANG Ce, XIE Yakun, LI Weilian, ZHANG Tianyi. Woodland Extraction of SPOT7 Image Based on Multi-scale Attention Mechanism and Edge Constraint[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210251
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