Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 887-892, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-887-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS
X. F. Sun and X. G. Lin Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, No. 28, Lianhuachixi Road, Haidian District, Beijing 100830, China
Keywords: Semantic labelling, Random forest, Conditional random field, Differential morphological profile, Ensemble learning Abstract. As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.
Conference paper (PDF, 1523 KB)


Citation: Sun, X. F. and Lin, X. G.: RANDOM-FOREST-ENSEMBLE-BASED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGES AND NDSM OVER URBAN AREAS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 887-892, https://doi.org/10.5194/isprs-archives-XLII-2-W7-887-2017, 2017.

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