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In this paper, we present a two-stage scheme for supervised classification of polarimetric interferometric synthetic aperture radar (PolInSAR) imagery. In the first stage, a regularized logistic regression classifier is employed to generate probability vectors of object labels with polarimetric and interferometric features, respectively. The soft outputs (probability map) of previous logistic classifier...
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...
In this study, we are going to focus on the exploration of color based features on labeling remote sensing images. The common widely used color descriptors are based on color histogram or Gaussian Mixture Models. However, the problem of these methods is to lack of the spatial layout information. We propose a new color description and matching approach, which allows to relax the assumption of independence...
Terrain classification using polarimetric SAR imagery has been a very active research field over recent years. Although lots of features have been proposed and many classifiers have been employed, there are few works on comparing these features and their combination with different classifiers. In this paper, we firstly evaluate and compare different features for classifying polarimetric SAR imagery...
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