High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

doi:10.3390/ijgi6080245

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 机器人视觉团队

	High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field
	Sun, Xiaofeng1,2 ; Lin, Xiangguo 3; Shen, Shuhan1,2 ; Hu, Zhanyi1,2
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2017-08-01
卷号	6 期号:8 页码:1-26
关键词	Semantic Labeling Random Forest Conditional Random Field Differential Morphological Profile Ensemble Learning
DOI	10.3390/ijgi6080245
文献子类	Article
英文摘要	As 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.
WOS关键词	SUPPORT VECTOR MACHINES ; IMAGE CLASSIFICATION ; FEATURE-EXTRACTION ; AERIAL IMAGES ; POINT CLOUDS ; LAND-COVER ; FUSION ; SEGMENTATION ; PROFILES ; SCENES
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
WOS记录号	WOS:000408868400017
资助机构	National Key R&D Program of China(2016YFB0502002) ; Natural Science Foundation of China(61632003 ; 61333015 ; 61473292 ; 41371405)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/20727]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
作者单位	1.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
推荐引用方式 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.