CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field
Sun, Xiaofeng1,2; Lin, Xiangguo3; Shen, Shuhan1,2; Hu, Zhanyi1,2
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
2017-08-01
Volume6Issue:8Pages:1-26
SubtypeArticle
AbstractAs an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three main stages: feature extraction, classification, and classified result refinement. In the feature extraction stage, both multispectral images and 3D geometry data are used, which utilizes the complementary information from multisource data. In the classification stage, to tackle the problems associated with too many training samples and take full advantage of the information in the large-scale dataset, a random forest (RF) ensemble learning strategy is proposed by combining several RF classifiers together. Finally, an improved fully connected conditional random field (FCCRF) graph model is employed to derive the contextual information to refine the classification results. Experiments on the ISPRS Semantic Labeling Contest dataset show that the presented 3-stage method achieves 86.9% overall accuracy, which is a new state-of-the-art non-CNN (convolutional neural networks)-based classification method.
KeywordSemantic Labeling Random Forest Conditional Random Field Differential Morphological Profile Ensemble Learning
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.3390/ijgi6080245
WOS KeywordSUPPORT VECTOR MACHINES ; IMAGE CLASSIFICATION ; FEATURE-EXTRACTION ; AERIAL IMAGES ; POINT CLOUDS ; LAND-COVER ; FUSION ; SEGMENTATION ; PROFILES ; SCENES
Indexed BySCI
Language英语
Funding OrganizationNational Key R&D Program of China(2016YFB0502002) ; Natural Science Foundation of China(61632003 ; 61333015 ; 61473292 ; 41371405)
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000408868400017
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20727
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, 19 Yuquan Rd, Beijing 100049, Peoples R China
3.Chinese Acad Surveying & Mapping, Inst Photogrammetry & Remote Sensing, 28 Lianhuachixi Rd, Beijing 100830, Peoples R China
Recommended Citation
GB/T 7714
Sun, Xiaofeng,Lin, Xiangguo,Shen, Shuhan,et al. High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(8):1-26.
APA Sun, Xiaofeng,Lin, Xiangguo,Shen, Shuhan,&Hu, Zhanyi.(2017).High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(8),1-26.
MLA Sun, Xiaofeng,et al."High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.8(2017):1-26.
Files in This Item: Download All
File Name/Size DocType Version Access License
IJGI17.pdf(18038KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun, Xiaofeng]'s Articles
[Lin, Xiangguo]'s Articles
[Shen, Shuhan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Xiaofeng]'s Articles
[Lin, Xiangguo]'s Articles
[Shen, Shuhan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Xiaofeng]'s Articles
[Lin, Xiangguo]'s Articles
[Shen, Shuhan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: IJGI17.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.