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Improving the Interpretability and Reliability of Regional Land Cover Classification by U-Net Using Remote Sensing Data

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Abstract

The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment. At present, refined land cover data are mainly obtained by manual visual interpretation, which has the problems of heavy workload and inconsistent interpretation scales. Deep learning has greatly improved the automatic processing and analysis of remote sensing data. However, the accurate interpretation of feature information from massive datasets remains a difficult problem in wide regional land cover classification. To improve the efficiency of deep learning-based remote sensing image interpretation, we selected multisource remote sensing data, assessed the interpretability of the U-Net model based on surface spatial scenes with different levels of complexity, and proposed a new method of stereoscopic accuracy verification (SAV) to evaluate the reliability of the classification result. The results show that classification accuracy is more highly correlated with terrain and landscape than with other factors related to image data, such as platform and spatial resolution. As the complexity of surface spatial scenes increases, the accuracy of the classification results mainly shows a fluctuating declining trend. We also find the distribution characteristics from the SAV evaluation results of different land cover types in each surface spatial scene. Based on the results observed in this study, we consider the distinction of interpretability and reliability in diverse ground object types and design targeted classification strategies for different surface scenes, which can greatly improve the classification efficiency. The key achievement of this study is to provide the theoretical basis for remote sensing information analysis and an accuracy evaluation method for regional land cover classification, and the proposed method can help improve the likelihood that intelligent interpretation can replace manual acquisition.

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References

  • Adam H, Chen L C, Papandreou G et al., 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, 801–818. doi: 10.1007-978-3-030-01234-2_49

  • Badrinarayanan V, Kendall A, Cipolla R, 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481–2495. doi: 10.1199-TAAMI.0016.2644615

    Article  Google Scholar 

  • Bicheron P, Defourny P, Brockmann C et al., 2011. GLOBCOVER: products description and validation report. Foro Mundial De La Salud, 17(3): 285–287.

    Google Scholar 

  • Carranza-García M, García-Gutiérrez J, Riquelme J C, 2019. A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sensing, 11(3): 274. doi: https://doi.org/10.3390/rs11030274

    Article  Google Scholar 

  • Cevikalp H, Benligiray B, Gerek O N, 2020. Semi-supervised robust deep neural networks for multi-label image classification. Pattern Recognition, 100: 107164. doi: 10.1016-j.patcog.2019.107164

    Article  Google Scholar 

  • Chen G S, Li C, Wei W et al., 2019. Fully convolutional neural network with augmented atrous spatial pyramid pool and fully connected fusion path for high resolution remote sensing image segmentation. Applied Sciences, 9(9): 1816. doi: 10.3390-app9091816

    Article  Google Scholar 

  • Chen Jun, Liao Anping, Chen Jin et al., 2017. 30-Meter global land cover data product- globe land30. Geomatics World, 24(1): 1–8. (in Chinese)

    Google Scholar 

  • Chen L C, Papandreou G, Kokkinos I et al., 2017. 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. doi: 10.1109-TPAMI.2017.2699184

    Article  Google Scholar 

  • Congalton R G, 1988. Using spatial autocorrelation analysis to explore the errors in maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 54(5): 587–592. doi: 10.1109-36.3037

    Google Scholar 

  • Congalton R G, 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1): 35–46. doi: 10.1016-0034-4257(91)90048-B

    Article  Google Scholar 

  • De Fries R S, Hansen M, Townshend J R G et al., 1998. Global land cover classifications at 8 km spatial resolution: the use of training data derived from landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(16): 3141–3168. doi: 10.1080-014311698214235

    Article  Google Scholar 

  • Gastaldo P, Zunino R, Heynderickx I et al., 2005. Objective quality assessment of displayed images by using neural networks. Signal Processing:Image Communication, 20(7): 643–661. doi: 10.1016-j.image.2005.03.013

    Google Scholar 

  • Gong P, Liu H, Zhang M N et al., 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64: 370–373. doi: 10.1016-j.scib.2019.03.002

    Article  Google Scholar 

  • Guo Chongzhou, Li Ke, Li He, 2020. Deep convolution neural network method for remote sensing image quality classification. Geomatics and Information Science of Wuhan University, 1–9. (in Chinese)

  • Guo R, Liu J B, Li N et al., 2018. Pixel-wise classification method for high resolution remote sensing imagery using deep neural networks. ISPRS International Journal of Geo-Information, 7(3): 110. doi: 10.3390-ijgi7030110

    Article  Google Scholar 

  • Guo Y M, Liu Y, Georgiou T et al., 2018. A review of semantic segmentation using deep neural networks. International Journal of Multimedia Information Retrieval, 7(2): 87–93. doi: 10.1007-s13735-017-0141-z

    Article  Google Scholar 

  • He T D, Wang S X, 2021. Multi-spectral remote sensing landcover classification based on deep learning methods. The Journal of Supercomputing, 77(3): 2829–2843. doi: 10.1007-s11227-020-03377-w

    Article  Google Scholar 

  • Hinton G E, Osindero S, Teh Y W, 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527–1554. doi: 10.1162-neco.2006.18.7.1527

    Article  Google Scholar 

  • Hong D F, Gao L R, Yokoya N et al., 2020. More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 59(5): 4340–4354. doi: 10.1109-TGRS.2020.3016820

    Article  Google Scholar 

  • Kussul N, Lavreniuk M, Skakun S et al., 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5): 778–782. doi: 10.1109-LGRS.2017.2681128

    Article  Google Scholar 

  • Li Deren, Zhang Liangpei, Xia Guisong, 2014. Automatic analysis and mining of remote sensing big data. Acta Geodaetica et Cartographica Sinica, 43(12): 1211–1216. (in Chinese)

    Google Scholar 

  • Loveland T R, Reed B C, Brown J F et al., 2000. Development of a global land cover characteristics database and IGBP DIS cover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7): 1303–1330. doi: 10.1080-014311600210191

    Article  Google Scholar 

  • Ma H J, Liu Y L, Ren Y H et al., 2020. Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images. Remote Sensing, 12(2): 260. doi: 10.3390-rs12020260

    Article  Google Scholar 

  • Meng X R, Zhang S Q, Zang S Y, 2018. Remote sensing classification of wetland communities based on convolutional neural networks and high resolution images: a case study of the Honghe wetland. Scientia Geographica Sinica, 38: 1914–1923. doi: 10.13249-j.cnki.sgs.2018.11.019

    Google Scholar 

  • Noh H, Hong S, Han B, 2015. Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, 1520–1528. doi: 10.1109-ICCV.2015.178

  • Pan X R, Gao L R, Zhang B et al., 2018. High-resolution aerial imagery semantic labeling with dense pyramid network. Sensors, 18(11): 3774. doi: 10.3390-s18113774

    Article  Google Scholar 

  • Pugh S A, Congalton, 2001. Applying spatial autocorrelation analysis to evaluate error in new England forest-cover-type maps derived from landsat thematic mapper data. Photogrammetric Engineering and Remote Sensing, 67(5): 613–620. doi: https://doi.org/10.1007/s001900100173

    Google Scholar 

  • Quartulli M, Olaizola I G, 2013. A review of EO image information mining. ISPRS journal of Photogrammetry and Remote Sensing, 75: 11–28. doi: 10.1016-j.isprsjprs.2012.09.010

    Article  Google Scholar 

  • Ronneberger O, Fischer P, Brox T, 2015. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 234–241. doi: 10.1007-978-3-319-24574-4_28

    Google Scholar 

  • Rezaee M, Mahdianpari M, Zhang Y et al., 2018. Deep convolutional neural network for complex wetland classification using optical remote sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(9): 3030–3039. doi: 10.1109-JSTARS.2018.2846178

    Article  Google Scholar 

  • Shamsolmoali P, Zareapoor M, Wang R et al., 2019. A novel deep structure U-Net for sea-land segmentation in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3219–3232. doi: 10.1109-JSTARS.2019.2925841

    Article  Google Scholar 

  • Shelhamer E, Long J, Darrell T, 2016. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640–651. doi: 10.1109-TPAMI.2016.2572683

    Article  Google Scholar 

  • Wang Yahui, Chen Erxue, Guo Ying et al., 2020. Deep U-net optimization method for forest type classification with high resolution multispectral remote sensing images. Forest Research, 33(1): 11–18. (in Chinese)

    Google Scholar 

  • Wang Z, Bovik A C, Sheikh H R et al., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600–612. doi: 10.1109-TIP.2003.819861

    Article  Google Scholar 

  • Weng Q H, 2011. Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications. New York: CRC Press.

    Book  Google Scholar 

  • Xu X D, Li W, Ran Q et al., 2017. Multisource remote sensing data classification based on convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 937–949. doi: 10.1109-TGRS.2017.2756851

    Article  Google Scholar 

  • Yuan Q Q, Shen H F, Li T W et al., 2020. Deep learning in environmental remote sensing: achievements and challenges. Remote Sensing of Environment, 241: 111716. doi: 10.1016-j.rse.2020.111716

    Article  Google Scholar 

  • Yuan T, Zheng X Q, Hu X et al., 2014. A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm. PloS One, 9(1): e86528. doi: 10.1371-journal.pone.0086528

    Article  Google Scholar 

  • Zhang L P, Zhang L P, Du B., 2016. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2): 22–40. doi: 10.1109-MGRS.2016.2540798

    Article  Google Scholar 

  • Zhang X, Liu L Y, Chen X D et al., 2021. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series landsat imagery. Earth System Science Data, 13(6): 2753–2776. doi: 10.5194-essd-13-2753-2021

    Article  Google Scholar 

  • Zhang Z X, Liu Q J, Wang Y H, 2018. Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters, 15(5): 749–753. doi: 10.1109-LGRS.2018.2802944

    Article  Google Scholar 

  • Zhao H S, Shi J P, Qi X J et al., 2017. Pyramid scene parsing network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6230–6239. doi: 10.1109/CVPR.2017.660

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Authors and Affiliations

  1. Shaanxi Geomatics Center of Ministry of Natural Resources, Xi’an, 710054, China

    Xinshuang Wang, Jiange Liu, Xiangwu Li, Feihang Zuo & Mu Bai

  2. The First Topographic Surveying Brigade of Ministry of Natural Resources, Xi’an, 710054, China

    Jiancheng Cao

  3. The First Geographic Information Cartography Institute of Ministry of Natural Resources, Xi’an, 710054, China

    Lu Wang

Authors
  1. Xinshuang Wang
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  2. Jiancheng Cao
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  3. Jiange Liu
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  4. Xiangwu Li
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  5. Lu Wang
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  6. Feihang Zuo
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  7. Mu Bai
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Correspondence to Jiancheng Cao or Mu Bai.

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Under the auspices of National Natural Science Foundation of China (No. 41971352), Key Research and Development Project of Shaanxi Province (No. 2022ZDLSF06-01)

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Wang, X., Cao, J., Liu, J. et al. Improving the Interpretability and Reliability of Regional Land Cover Classification by U-Net Using Remote Sensing Data. Chin. Geogr. Sci. 32, 979–994 (2022). https://doi.org/10.1007/s11769-022-1315-z

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  • DOI: https://doi.org/10.1007/s11769-022-1315-z

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