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Abstrakty
Satellite image classification is essential for many socio-economic and environmental applications of geographic information systems, including urban and regional planning, conservation and management of natural resources, etc. In this paper, we propose a deep learning architecture to perform the pixel-level understanding of high spatial resolution satellite images and apply it to image classification tasks. Specifically, we augment the spatial pyramid pooling module with image-level features encoding the global context, and integrate it into the U-Net structure. The proposed model solves the problem consisting in the fact that U-Net tends to lose object boundaries after multiple pooling operations. In our experiments, two public datasets are used to assess the performance of the proposed model. Comparison with the results from the published algorithms demonstrates the effectiveness of our approach.
Rocznik
Tom
Strony
399--413
Opis fizyczny
Bibliogr. 53 poz., rys., tab.
Twórcy
autor
- School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China
autor
- School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China
autor
- School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China
Bibliografia
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- [22] Kim, J.H., Lee, H., Hong, S.J., Kim, S., Park, J., Hwang, J.Y. and Choi, J.P. (2018). Objects segmentation from high-resolution aerial images using U-Net with pyramid pooling layers, IEEE Geoscience and Remote Sensing Letters 16(1): 115-119.
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- [28] Marcos, D., Volpi, M., Kellenberger, B. and Tuia, D. (2018). Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models, ISPRS Journal of Photogrammetry and Remote Sensing 145(5): 96–107.
- [29] Marmanis, D., Schindler, K., Wegner, J., Galliani, S., Datcu, M. and Stilla, U. (2016). Classification with an edge: Improving semantic image segmentation with boundary detection, ISPRS Journal of Photogrammetry and Remote Sensing 135(7): 158–172.
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- [32] Noh, H., Hong, S. and Han, B. (2015). Learning deconvolution network for semantic segmentation, IEEE International Conference on Computer Vision, Santiago, Chile, pp. 1520–1528.
- [33] Peng, D., Zhang, Y. and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++, Remote Sensing 11(11): 1382.
- [34] Pohlen, T., Hermans, A., Mathias, M. and Leibe, B. (2017). Full-resolution residual networks for semantic segmentation in street scenes, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 4151–4160.
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- [44] Tao, L., Abd-Elrahman, A., Morton, J. and Wilhelm, V.L. (2018). Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system, GIScience & Remote Sensing 55(2): 243–264.
- [45] Vemulapalli, R., Tuzel, O., Liu, M.Y. and Chellappa, R. (2016). Gaussian conditional random field network for semantic segmentation, Computer Vision & Pattern Recognition, Las Vegas, NV, USA, pp. 3224–3233.
- [46] Volpi, M. and Tuia, D. (2017). Dense semantic labeling of subdecimeter resolution images with convolutional neural networks, IEEE Transactions on Geoscience and Remote Sensing 55(2): 881–893.
- [47] Yang, H., Yu, B., Luo, J. and Chen, F. (2019). Semantic segmentation of high spatial resolution images with deep neural networks, GIScience & Remote Sensing 56(5): 1–20.
- [48] Zhang, C., Xin, P., Li, H., Gardiner, A. and Atkinson, P.M. (2018a). A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification, ISPRS Journal of Photogrammetry & Remote Sensing 140(7): 133–144.
- [49] Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P. and Zhang, S. (2018b). Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery, IEEE Transactions on Geoscience & Remote Sensing 18(11): 3717.
- [50] Zhao, H., Shi, J., Qi, X., Wang, X. and Jia, J. (2017). Pyramid scene parsing network, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 6230–6239.
- [51] Zhao, W. and Du, S. (2016). Learning multiscale and deep representations for classifying remotely sensed imagery, ISPRS Journal of Photogrammetry & Remote Sensing 113(3): 155–165.
- [52] Zhou, B., Hang, Z., Puig, X., Fidler, S., Barriuso, A. and Torralba, A. (2017). Scene parsing through ADE20K dataset, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 633–641.
- [53] Zhuowen, T. and Xiang, B. (2010). Auto-context and its application to high-level vision tasks and 3D brain image segmentation, IEEE Transactions on Pattern Analysis & Machine Intelligence 32(10): 1744–1757.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9d6dffc1-9163-451f-a2cc-c676d13c30ed