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

doi:10.3390/ijgi6080245

	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
发表期刊	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2017-08-01
卷号	6 期号:8 页码:1-26
文章类型	Article
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.
关键词	Semantic Labeling Random Forest Conditional Random Field Differential Morphological Profile Ensemble Learning
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.3390/ijgi6080245
关键词[WOS]	SUPPORT VECTOR MACHINES ; IMAGE CLASSIFICATION ; FEATURE-EXTRACTION ; AERIAL IMAGES ; POINT CLOUDS ; LAND-COVER ; FUSION ; SEGMENTATION ; PROFILES ; SCENES
收录类别	SCI
语种	英语
项目资助者	National Key R&D Program of China(2016YFB0502002) ; Natural Science Foundation of China(61632003 ; 61333015 ; 61473292 ; 41371405)
WOS研究方向	Physical Geography ; Remote Sensing
WOS类目	Geography, Physical ; Remote Sensing
WOS记录号	WOS:000408868400017
引用统计	被引频次：30[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20727
专题	多模态人工智能系统全国重点实验室_机器人视觉
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
第一作者单位	模式识别国家重点实验室
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.

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