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

by 1,2, 3, 1,2,* and 1,2
1
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Beijing 100190, China
2
University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing 100049, China
3
Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachixi Road, Beijing 100830, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(8), 245; https://doi.org/10.3390/ijgi6080245
Received: 5 May 2017 / Revised: 29 July 2017 / Accepted: 2 August 2017 / Published: 10 August 2017
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. View Full-Text
Keywords: semantic labeling; random forest; conditional random field; differential morphological profile; ensemble learning semantic labeling; random forest; conditional random field; differential morphological profile; ensemble learning
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Sun, X.; Lin, X.; Shen, S.; Hu, Z. High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field. ISPRS Int. J. Geo-Inf. 2017, 6, 245.

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