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Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, Gong1; Han, Junwei1; Lu, Xiaoqiang2
Department光学影像学习与分析中心
2017-10-01
Source PublicationPROCEEDINGS OF THE IEEE
ISSN0018-9219
Volume105Issue:10Pages:1865-1883
Contribution Rank2
Abstract

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.

SubtypeArticle
KeywordBenchmark Data Set Deep Learning Handcrafted Features Remote Sensing Image Scene Classification Unsupervised Feature Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/JPROC.2017.2675998
Indexed BySCI ; EI
WOS KeywordGEOSPATIAL OBJECT DETECTION ; LAND-USE CLASSIFICATION ; LOCAL BINARY PATTERNS ; VISUAL-WORDS MODEL ; HIGH-RESOLUTION ; SATELLITE IMAGES ; TARGET DETECTION ; FEATURE-SELECTION ; NEURAL-NETWORKS ; GIST FEATURES
Language英语
WOS Research AreaEngineering
Funding OrganizationNational Science Foundation of China(61401357 ; Fundamental Research Funds for the Central Universities(3102016ZY023) ; 61522207 ; 61473231)
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000411273300004
Citation statistics
Cited Times:199[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/29357
Collection光学影像学习与分析中心
Affiliation1.Northwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China
2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Cheng, Gong,Han, Junwei,Lu, Xiaoqiang. Remote Sensing Image Scene Classification: Benchmark and State of the Art[J]. PROCEEDINGS OF THE IEEE,2017,105(10):1865-1883.
APA Cheng, Gong,Han, Junwei,&Lu, Xiaoqiang.(2017).Remote Sensing Image Scene Classification: Benchmark and State of the Art.PROCEEDINGS OF THE IEEE,105(10),1865-1883.
MLA Cheng, Gong,et al."Remote Sensing Image Scene Classification: Benchmark and State of the Art".PROCEEDINGS OF THE IEEE 105.10(2017):1865-1883.
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