Deep Learning Methods Used in Remote Sensing Images: A Review

Authors

  • Ekram M. Rewhel

    School of Software Engineering, Beijing University of Science and Technology, Beijing, 100083, China
  • Jianqiang Li

    School of Software Engineering, Beijing University of Science and Technology, Beijing, 100083, China
  • Amal A. Hamed

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Hatem M. Keshk

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Amira S. Mahmoud

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Sayed A. Sayed

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Ehab Samir

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Hind H. Zeyada

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Sayed A. Mohamed

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Marwa S. Moustafa

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Ayman H. Nasr

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt
  • Ashraf K. Helmy

    Data Reception, Analysis and Receiving Station Division, National Authority for Remote Sensing and Space Science, Cairo, 1564, Egypt

DOI:

https://doi.org/10.30564/jees.v5i1.5232
Received: 3 November 2022 | Revised: 9 February 2023 | Accepted: 20 February 2023 | Published Online: 4 April 2023

Abstract

Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model's success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.

Keywords:

Deep Learning (DL); Satellite imaging; Image classification; Segmentation and object detection

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How to Cite

Rewhel, E. M., Li, J., Hamed, A. A., Keshk, H. M., Mahmoud, A. S., Sayed, S. A., Samir, E., Zeyada, H. H., Mohamed, S. A., Moustafa, M. S., Nasr, A. H., & Helmy, A. K. (2023). Deep Learning Methods Used in Remote Sensing Images: A Review. Journal of Environmental & Earth Sciences, 5(1), 33–64. https://doi.org/10.30564/jees.v5i1.5232

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Vol. 5 , Iss. 1 (April 2023)

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Review

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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