Urban land use/cover classification of remote sensing using random forests under the framework of conditional random fields
YANG Yun1,2, XU Li3, YAN Peili4
1. College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China;
2. Key Laboratory for Western Mineral Resources and Engineering of Ministry of Education, Chang'an University, Xi'an 710054, China;
3. College of Information Engineering, Chang'an University, Xi'an 710061, China;
4. Xi'an Changqing Technology and Engineering Co., Ltd., Xi'an 710018, China
The classification accuracy of superpixel-based conditional random fields(CRFs) model greatly depends on segmentation scale parameters, which constitutes a problem that should be solved. Therefore, to answer the question "whether a pixel-based CRFs model performs well in HSR image classification with m level spatial resolution or not",the authors proposed a pixel-based CRFs model with the association term defined as an output of random forests classifier and the interaction potential defined as Potts function weighted by contrast function, and the definition of association and interaction terms adopted multi-cue features such as histogram of gradient, multi- scale and multi-direction Texton filter and multi-spectral information from HSR imagery. Finally, the proposed model was trained using piecewise training method and inferred using α-expansion algorithm based on graph cut. Experiments on a typical urban scene from QuickBird multi-spectral satellite imagery have shown that the proposed RF-CRFs model shows the classification accuracy of over 82.52%. In addition, the classification accuracy of the model is higher than that of the RF classifier by 3.35% on average.
杨耘, 徐丽, 颜佩丽. 条件随机场框架下基于随机森林的城市土地利用/覆盖遥感分类[J]. 国土资源遥感, 2014, 26(4): 51-55.
YANG Yun, XU Li, YAN Peili. Urban land use/cover classification of remote sensing using random forests under the framework of conditional random fields. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 51-55.
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2014, Vol. 26 
