Research on remote sensing image extraction based on deep learning

被引：0
作者：
Shun Z. [1 ]
Li D. [1 ]
Jiang H. [1 ]
Li J. [1 ]
Peng R. [1 ]
Lin B. [1 ]
Liu Q. [1 ]
Gong X. [1 ]
Zheng X. [1 ]
Liu T. [1 ]
机构：
[1] Sichuan Agricultural University, College of Information Engineering, Sichuan, Yaan
来源：
PeerJ Computer Science | 2022年 / 8卷
关键词：
Attention mechanism; Automatic extraction; Band fusion; Semantic segmentation; Sliding window prediction;
D O I：
10.7717/PEERJ-CS.847
中图分类号：
学科分类号：
摘要：
Remote sensing technology has the advantages of fast information acquisition, short cycle, and a wide detection range. It is frequently used in surface resource monitoring tasks. However, traditional remote sensing image segmentation technology cannot make full use of the rich spatial information of the image, the workload is too large, and the accuracy is not high enough. To address these problems, this study carried out atmospheric calibration, band combination, image fusion, and other data enhancement methods for Landsat 8 satellite remote sensing data to improve the data quality. In addition, deep learning is applied to remote-sensing image block segmentation. An asymmetric convolution-CBAM (AC-CBAM) module based on the convolutional block attention module is proposed. This optimization module of the integrated attention and sliding window prediction method is adopted to effectively improve the segmentation accuracy. In the experiment of test data, the mIoU, mAcc, and aAcc in this study reached 97.34%, 98.66%, and 98.67%, respectively, which is 1.44% higher than that of DNLNet (95.9%). The AC-CBAM module of this research provides a reference for deep learning to realize the automation of remote sensing land information extraction. The experimental code of our AC-CBAM module can be found at https://github.com/LinB203/remotesense. © 2022 Shun et al
引用
收藏
相关论文
共 39 条
  • [1] Badrinarayanan V, Kendall A, Cipolla R., Segnet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 12, pp. 2481-2495, (2017)
  • [2] Bahdanau D, Cho K, Bengio Y., Neural machine translation by jointly learning to align and translate, (2014)
  • [3] Ball JE, Bruce LM., Level set hyperspectral image classification using best band analysis, IEEE Transactions on Geoscience and Remote Sensing, 45, 10, pp. 3022-3027, (2007)
  • [4] Besag J., On the statistical analysis of dirty pictures, Journal of the Royal Statistical Society: Series B, 48, 3, pp. 259-279, (1986)
  • [5] Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL., Semantic image segmentation with deep convolutional nets and fully connected CRFs, (2014)
  • [6] Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL., Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 4, pp. 834-848, (2017)
  • [7] Chen LC, Papandreou G, Schroff F, Adam H., Rethinking atrous convolution for semantic image segmentation, (2017)
  • [8] Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H., Encoder-decoder with atrous separable convolution for semantic image segmentation, Proceedings of the European Conference on Computer Vision (ECCV), pp. 801-818, (2018)
  • [9] Cheng G, Lang C, Wu M, Xie X, Yao X, Han J., Feature enhancement network for object detection in optical remote sensing images, Journal of Remote Sensing, 2021, pp. 1-14, (2021)
  • [10] Cheng G, Yang C, Yao X, Guo L, Han J., When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs, IEEE Transactions on Geoscience and Remote Sensing, 56, 5, pp. 2811-2821, (2018)
1234