Remote Sensing Image Scene Classification: Benchmark and State of the Art
Open accessAbstract
Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various data sets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning data sets and methods for scene classification is still lacking. In addition, almost all existing data sets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale data set, termed “NWPU-RESISC45,” which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This data set contains 31 500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 1) is large-scale on the scene classes and the total image number; 2) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion; and 3) has high within-class diversity and between-class similarity. The creation of this data set will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed data set, and the results are reported as a useful baseline for future research.
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The NWPU-RESISC45 dataset contains 31,500 satellite images across 45 classes.
“The satellite images come from the NWPU-RESISC45 dataset [16], which has 31,500 satellite images across 45 classes.”
From Improving Human-AI Collaboration With Descriptions of AI Behavior (2023) · cites this paper as [16]
Cloud and glacier images are areas of high error in the confusion matrices for the NWPU-RESISC45 dataset.
“The BD groups were chosen from areas of high error in the confusion matrices from the original paper [16]: Group 1 - Cloud and glacier images Group 2 - Meadow and golf course images”
From Improving Human-AI Collaboration With Descriptions of AI Behavior (2023) · cites this paper as [16]
Meadow and golf course images are areas of high error in the confusion matrices for the NWPU-RESISC45 dataset.
“The BD groups were chosen from areas of high error in the confusion matrices from the original paper [16]: Group 1 - Cloud and glacier images Group 2 - Meadow and golf course images”
From Improving Human-AI Collaboration With Descriptions of AI Behavior (2023) · cites this paper as [16]
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“The satellite images come from the NWPU-RESISC45 dataset [16], which has 31,500 satellite images across 45 classes.”
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- Authors
- Cheng, G., Han, J., & Lu, X.
- Affiliations
- Northwestern Polytechnical University (CN)
- Chinese Academy of Sciences (CN)
- Venue
- Proceedings of the IEEE
- Published
- 2017-10
- Volume
- 105
- Issue
- 10
- Pages
- 1865-1883
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2592962403
- Cited by
- 2,926
- References
- 11
- Citations by year
- 2026: 365 · 2025: 456 · 2024: 416 · 2023: 395 · 2022: 324 · 2021: 335 · 2020: 283 · 2019: 190 · 2018: 128 · 2017: 34
- FWCI
- 144.19
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (acceptedVersion)
- Institutional Repository of Xi'an Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Xian Institute of Optics and Precision Mechanics) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation of China (61401357, 61522207, 61473231)
- Fundamental Research Funds for the Central Universities (3102016ZY023)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- remote sensing scene classification, NWPU-RESISC45, benchmark dataset, viewpoint variation, illumination variation, occlusion, deep learning approach
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