Open Access
Issue
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
Article Number 03002
Number of page(s) 16
Section Deep Learning
DOI https://doi.org/10.1051/itmconf/20235603002
Published online 09 August 2023
  1. Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., Dickinson, R., 2013. The role of satellite remote sensing in climate change studies. Nat. Clim. Chang. 3, 875-88 [CrossRef] [Google Scholar]
  2. Overpeck, J.T., Meehl, G.A., Bony, S., Easterling, D.R., 2011. Climate data challenges in the 21st century. Science 331, 700. [CrossRef] [Google Scholar]
  3. Liang, S., 2005. Quantitative Remote Sensing of Land Surfaces. John Wiley & Sons 30. [Google Scholar]
  4. LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436. [CrossRef] [PubMed] [Google Scholar]
  5. Bengio, Y., Courville, A., Vincent, P., 2013. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828. [CrossRef] [Google Scholar]
  6. Zhang, L., Zhang, L., Du, B., 2016b. Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine 4, 22-40 [CrossRef] [Google Scholar]
  7. Ball, J. E., Anderson, D. T., & Chan, C. S. (2017). A comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. Journal of applied remote sensing, 11(4), 042609–042609. [CrossRef] [Google Scholar]
  8. Shen, C., 2018. Deep learning: a next-generation big-data approach for hydrology. EOS 99, 1. [CrossRef] [Google Scholar]
  9. Chlingaryan, A., Sukkarieh, S., Whelan, B., 2018. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput. Electron. Agric. 151, 61–69. [CrossRef] [Google Scholar]
  10. Blaschke, T., 2010. Object-based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16. [CrossRef] [Google Scholar]
  11. Bo Huang, Bei Zhao, Yimeng Song, Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imageryRemote Sensing of Environment, Volume 214, 2018, Pages 73-86, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2018.04.050. [Google Scholar]
  12. Scott, G.J., Marcum, R.A., Davis, C.H., Nivin, T.W., 2017b. Fusion of deep convolutional neural networks for land cover classification of high-resolution imagery. IEEE Geosci. Remote Sens. Lett. 14, 1638–1642. [CrossRef] [Google Scholar]
  13. Shen, H., Meng, X., Zhang, L., 2016. An integrated framework for the spatial- temporal spectral fusion of remote sensing images. IEEE Trans. Geosci. Remote Sens. 54, 7135–7148. [CrossRef] [Google Scholar]
  14. Peng, J., Loew, A., Merlin, O., Verhoest, N.E.C., 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 55, 341–366. [CrossRef] [Google Scholar]
  15. Shen, H., Li, X., Cheng, Q., Zeng, C., Yang, G., Li, H., Zhang, L., 2015. Missing information reconstruction of remote sensing data: a technical review. IEEE Geoscience and Remote Sensing Magazine 3, 61–85. [CrossRef] [Google Scholar]
  16. Zeng, C., Shen, H., Zhang, L., 2013. Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method. Remote Sens. Environ. 131, 182–194. [CrossRef] [Google Scholar]
  17. Cheng, Q., Shen, H., Zhang, L., Yuan, Q., Zeng, C., 2014. Cloud removal for remotely sensed images by similar pixel replacement guided with a spatiotemporal MRF model. ISPRS J. Photogramm. Remote Sens. 92, 54–68. [CrossRef] [Google Scholar]
  18. Zeng, C., Long, D., Shen, H., Wu, P., Cui, Y., Hong, Y., 2018. A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud. ISPRS J. Photogramm. Remote Sens. 141, 30–45. [CrossRef] [Google Scholar]
  19. Das, M., Ghosh, S.K., 2017. A deep-learning-based forecasting ensemble to predict missing data for remote sensing analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, 5228–5236. [CrossRef] [Google Scholar]
  20. Li, J., Du, Q. & Li, Y. An efficient radial basis function neural network for hyperspectral remote sensing image classification. Soft Comput 20, 4753-4759 (2016). https://doi.org/10.1007/s00500-015-1739-9 [CrossRef] [Google Scholar]
  21. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69. [CrossRef] [Google Scholar]
  22. Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1335–1343. [CrossRef] [Google Scholar]
  23. Huang, X., Li, M., Li, X., & Huang, X. (2017). Integration of self-organizing map neural network and random forest for crop classification from high-resolution remote sensing. International Journal of Remote Sensing, 38(14), 4057–4075. [Google Scholar]
  24. Chen, Q., Zhao, S., Liu, J., & Liu, J. (2015). An Improved Self-Organizing Map for Remote Sensing Image Segmentation. Remote Sensing, 7(3), 3210–3232. [Google Scholar]
  25. Diao, W., Sun, X., Zheng, X., Dou, F., Wang, H., Fu, K., 2016. Efficient saliency- based object detection in remote sensing images using deep belief networks. IEEE Geosci. Remote Sens. Lett. 13, 137-141 [CrossRef] [MathSciNet] [Google Scholar]
  26. Fang, K., Pan, M., Shen, C., 2019. The value of SMAP for long-term soil moisture estimation with the help of deep learning. IEEE Trans. Geosci. Remote Sens. 57, 2221–2233. [CrossRef] [Google Scholar]
  27. Ndikumana, E., Dinh Ho Tong, M., Baghdadi, N., Courault, D., Hossard, L., 2018a. Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens. 10 [Google Scholar]
  28. Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., Feng, M., 2019. Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time series-a case study in Zhanjiang, China. Remote Sens. 11. [Google Scholar]
  29. Augusteijn, M.F., Warrender, C.E., 1998. Wetland classification using optical and radar data and neural network classification. Int. J. Remote Sens. 19, 1545–1560. [CrossRef] [Google Scholar]
  30. Shaker, A., Yan, W.Y., LaRocque, P.E., 2019. Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments. ISPRS J. Photogramm. Remote Sens. 152, 94–108. [CrossRef] [Google Scholar]
  31. Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q., 2011. Perpixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115, 1145-1161 [CrossRef] [Google Scholar]
  32. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A., 2019. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J. Photogramm. Remote Sens. 152, 166–177. [CrossRef] [Google Scholar]
  33. Zhang, C., Pan, X., Li, H., Gardiner, A., Sargent, I., Hare, J., Atkinson, P.M., 2018a. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS J. Photogramm. Remote Sens. 140, 133-144 [CrossRef] [Google Scholar]
  34. Zeiler, M.D., Fergus, R., 2014. Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. Springer, pp. 818-833. [Google Scholar]
  35. Scott, G.J., England, M.R., Starms, W.A., Marcum, R.A., Davis, C.H., 2017a. Training deep convolutional neural networks for land-cover classification of high- resolution imagery. IEEE Geosci. Remote Sens. Lett. 14, 549–553. [CrossRef] [Google Scholar]
  36. Huang, B., Zhao, B., Song, Y., 2018. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens. Environ. 214, 73–86. [CrossRef] [Google Scholar]
  37. Ienco, D., Interdonato, R., Gaetano, R., Minh, D.H.T., 2019. Combining Sentinel- 1 and Sentinel-2 satellite image time series for land cover mapping via a multi- source deep learning architecture. ISPRS J. Photogramm. Remote Sens. 158, 11–22. [CrossRef] [Google Scholar]
  38. Interdonato, R., Ienco, D., Gaetano, R., Ose, K., 2019. Duplo: a DUal view Point deep Learning architecture for time series classification. ISPRS J. Photogramm. Remote Sens. 149, 91–104. [CrossRef] [Google Scholar]
  39. Lary, D., Remer, L., MacNeill, D., Roscoe, B., Paradise, S., 2009. Machine learning and Bias correction of MODIS aerosol optical depth. IEEE Geosci. Remote Sens. Lett. 6, 694-698 [CrossRef] [Google Scholar]
  40. Ristovski, K., Vucetic, S., Obradovic, Z., 2012. Uncertainty analysis of neural- network-based aerosol retrieval. IEEE Trans. Geosci. Remote Sens. 50, 409-414 [CrossRef] [Google Scholar]
  41. Lanzaco, B.L., Olcese, L.E., Palancar, G.G., Toselli, B.M., 2017. An improved aerosol optical depth map based on machine-learning and MODIS data: development and application in South America. Aerosol Air Qual. Res. 17, 1623-1636 [CrossRef] [Google Scholar]
  42. Qin, W., Wang, L., Lin, A., Zhang, M., Bilal, M., 2018. Improving the estimation of daily aerosol optical depth and aerosol radiative effect using an optimized artificial neural network. Remote Sens. 10, 1022 [CrossRef] [MathSciNet] [Google Scholar]
  43. Tapiador, F.J., Kidd, C., Hsu, K.L., Marzano, F., 2004. Neural networks in satellite rainfall estimation. Meteorol. Appl. 11, 83–91. [CrossRef] [Google Scholar]
  44. Huang, B., Zhao, B., Song, Y., 2018. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens. Environ. 214, 73-86 [CrossRef] [Google Scholar]
  45. Tao, Y., Gao, X., Ihler, A., Sorooshian, S., Hsu, K., 2017. Precipitation identification with bispectral satellite information using deep learning approaches. J. Hydrometeorol. 18, 1271–1283. [CrossRef] [Google Scholar]
  46. Chen, H., Chandrasekar, V., Tan, H., Cifelli, R., 2019b. Rainfall estimation from ground radar and TRMM precipitation radar using hybrid deep neural networks. Geophys. Res. Lett. 46, 10669-10678 [CrossRef] [Google Scholar]
  47. Xie, Y., Sha, Z., Yu, M., Bai, Y., Zhang, L., 2009. A comparison of two models with Landsat data for estimating above-ground grassland biomass in Inner Mongolia, China. Ecol. Model. 220, 1810–1818. [CrossRef] [Google Scholar]
  48. Davis, D.T., Chen, Z., Tsang, L., Hwang, J.-N., Chang, A.T., 1993. Retrieval of snow parameters by iterative inversion of a neural network. IEEE Trans. Geosci. Remote Sens. 31, 842–852. [CrossRef] [Google Scholar]
  49. Nijhawan, R., Das, J., Raman, B., 2018. A hybrid of deep learning and hand- crafted features-based approach for snow cover mapping. Int. J. Remote Sens. 1-15. [Google Scholar]
  50. Shen, H., Jiang, Y., Li, T., Cheng, Q., Zeng, C., Zhang, L., 2020. Deep Learning- based Air Temperature Mapping by Fusing Remote Sensing, Station, Simulation, and Socioeconomic Data. (arXiv:2001.04650). [Google Scholar]
  51. Schütt, K.T., Arbabzadah, F., Chmiela, S., Müller, K.R., Tkatchenko, A., 2017. Quantumchemical insights from deep tensor neural networks. Nat. Commun. 8, 13890. [CrossRef] [Google Scholar]
  52. Karpatne, A., Watkins, W., Read, J., Kumar, V., 2017. Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling. (arXiv preprint arXiv:1710.11431). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.