Deep learning for remote sensing image classification: A survey

By Ying Li, Haokui Zhang, Xizhe Xue, Yenan Jiang, Qiang Shen. WIREs Data Mining Knowl. Discov., 2018

455 citations recorded by the source corpus.

Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel‐wise and scene‐wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL‐based RS methods is also provided. Finally, the challenges and potential directions for further research are discussed.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/fdb7c847a947c2249882cabf0ba4bf070c6c667e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Zhenwei Shi

In Computer Vision and Pattern Recognition

1. Basant Kumar
2. Keyan Chen
3. Zhenwei Shi
4. Chenyang Liu
5. Wenyuan Li
6. Amit Kumar Singh
7. Bo-Ying Chen
8. Moez Krichen

In Media Technology

1. Keyan Chen
2. Zhenwei Shi
3. Wenyuan Li
4. Zhengxia Zou
5. Simone Scardapane
6. Valerio Marsocci
7. Sheng Fang
8. Zhe Li

Citations in the corpus

• Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives (2021, methodology): “[16] Hyperspectral image classification CNN MSRA (for Microsoft Research Asia)”
• Review on remote sensing methods for landslide detection using machine and deep learning (2020, background): “71 (c .”
• RSMamba: Remote Sensing Image Classification With State Space Model (2024, methodology): “Early methodologies predominantly focused on feature construction, extraction, and selection, investigating feature engineering machine learning methods represented by SIFT, LBP, color histograms, GIST, BoVW [7], etc.”
• A Review on Deep Learning in UAV Remote Sensing (2021, unknown): “Additionally, a survey performed by Li et al. [114] helped to understand some DL applications regarding the overall performance of DNNs in publicly available datasets for image classification task.”
• Deep Learning for Hyperspectral Image Classification: An Overview (2019, unknown): “HSIs in the remote sensing field [50]–[52].”