亚洲彩票app下载

OPT OpenIR  > 光谱成像技术研究室
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, Gong1; Han, Junwei1; Lu, Xiaoqiang2
作者部门光学影像学习与分析中心
2017-10-01
发表期刊PROCEEDINGS OF THE IEEE
ISSN0018-9219
卷号105期号:10页码:1865-1883
产权排序2
摘要

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.

文章类型Article
关键词Benchmark Data Set Deep Learning Handcrafted Features Remote Sensing Image Scene Classification Unsupervised Feature Learning
WOS标题词Science & Technology ; Technology
DOI10.1109/JPROC.2017.2675998
收录类别SCI ; EI
关键词[WOS]GEOSPATIAL OBJECT DETECTION ; LAND-USE CLASSIFICATION ; LOCAL BINARY PATTERNS ; VISUAL-WORDS MODEL ; HIGH-RESOLUTION ; SATELLITE IMAGES ; TARGET DETECTION ; FEATURE-SELECTION ; NEURAL-NETWORKS ; GIST FEATURES
语种英语
WOS研究方向Engineering
项目资助者National Science Foundation of China(61401357 ; Fundamental Research Funds for the Central Universities(3102016ZY023) ; 61522207 ; 61473231)
WOS类目Engineering, Electrical & Electronic
WOS记录号WOS:000411273300004
引用统计
被引频次:225[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊亚洲彩票app下载
条目标识符/handle/181661/29357
专题光谱成像技术研究室
作者单位1.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
推荐引用方式
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.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Remote Sensing Image(1718KB)期刊亚洲彩票app下载出版稿限制开放CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Cheng, Gong]的文章
[Han, Junwei]的文章
[Lu, Xiaoqiang]的文章
百度学术
百度学术中相似的文章
[Cheng, Gong]的文章
[Han, Junwei]的文章
[Lu, Xiaoqiang]的文章
必应学术
必应学术中相似的文章
[Cheng, Gong]的文章
[Han, Junwei]的文章
[Lu, Xiaoqiang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。