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To achieve the aim of classification for Polarimetrie synthetic aperture radar (PolSAR) images, the supervised classification approach based on sparse coding of covariance matrix is proposed in this paper. Being different from traditional classification methods which are based on polarization features extraction or statistical distribution models, our method research the sparse coding algorithm for...
Unsupervised classification of synthetic aperture radar (SAR) imagery is an essential step in SAR image interpretation. There is a growing demand for an efficient way to fuse multi-information of SAR imagery. This paper presents an intensity/coherent information fusion algorithm by using region covariance features for unsupervised classification. More precisely, we firstly extract the intensity properties...
Lots of SAR polarimetric features have been proposed to discriminate the different scattering processes of earth terrain. Using the full set of these features for classification is computationally too expensive and some of the features may be irrelevant to the classification task and other may be redundant. Thus, it is useful to exploit the discriminative power offered by a selection and combination...
Nowadays, many countries have established spontaneous reporting systems (SRSs) to facilitate postmarketing surveillance of listed drugs and collect enough data for detecting unknown adverse drug reactions. Due to data in SRSs coming from different sources of reporters, there heralds the problem of duplicate reporting; even a small amount of duplicate records would bias the detection results. Although...
In this paper, we propose a novel scheme of polarimetric synthetic aperture radar (PolSAR) image classification. We apply Laplacian eigenmaps (LE), a nonlinear dimensionality reduction (NDR) technique, to a high-dimensional polarimetric feature representation for PolSAR land-cover classification. A wide variety of polarimetric signatures are chosen to construct a high-dimensional polarimetric manifold...
In this paper we investigate the classification performance of the compact polarimetric interferometric SAR (C-PolInSAR). The stressed compact modes are π/4 mode and CTLR mode, due to DCP mode equivalent to CTLR mode in theory. First, we provide a state-of-art of the C-PolInSAR modes, and present the different reconstruction algorithms aiming at recovering the full PolInSAR information from the observed...
In this paper, we attempt to solve the efficiency problem of PolSAR scene classification with non-parametric classifier. We employ the tree-structure based search strategy to perform fast approximate nearest neighbour search by introducing the multiple randomized kd-tree and hierarchical kmeans-tree into ONBNN classifier. The experimental results on RadarSat-2 PolSAR dataset demonstrate that our method...
Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to...
In this paper, we present a study of extracting urban areas from Polarimetric Synthetic Aperture Radar (PolSAR) images using only positive samples. We solve this problem by learning a standard binary classifier (urban/non-urban) given an incomplete set of positive samples (urban) and a set of unlabeled samples (some of which are urban and some of which are non-urban) based on the work of Elkan and...
This study aimed at training artificial neural networks (ANN) to predict survival time in cancer patients by using microarray and clinical data. We analyzed public microarrays and clinical data sets in different kinds of cancer. We selected 15-30 genes (correlation coefficient>0.4) as ANN variables to train networks. The results shows ANN can predict survival time from Microarray data gene expression...
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...
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