Supervised land-cover classification of TerraSAR-X imagery over urban areas using extremely randomized clustering forests
2009 Joint Urban Remote Sensing Event(2009)
摘要
This study investigates the impact of the use of scattering intensity and texture features derived from TerraSAR-X intensity images on urban land cover classification accuracy, in combination with the Extremely Randomized Clustering Forests as the visual codebook former and classifier. We propose a multi-orientation ratio descriptor to represent the features of each SAR image patch effectively, and introduce a graph cut optimization based Markov Random Field smoothing processing to reduce block boundary effects due to patch-based classification method. We compare our classification results using one or all features together on 1m resolution TerraSAR-X images and show that the reasonableness of the proposed descriptor and the effectiveness of the Extremely Randomized Clustering Forests classifier. ©2009 IEEE.
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