Polarimetric synthetic aperture radar (PolSAR) data contain a large amount of potential information that is very appropriate for terrain classification. In this paper we proposed sparse representation approach to classify PolSAR data. Among a large number of PolSAR parameters we have chosen the most optimum parameters to form feature vector. Using k-means algorithm feature space is divided to C...

This paper proposes a method of polarimetric synthetic aperture radar (PolSAR) image classification using improved superpixel segmentation and manifold learning. Firstly, a 27-dimension polarimetric feature space is extracted by simple arithmetic operations of polarimetric parameters and polarimetric target decomposition. Secondly, Laplacian Eigenmap (LE) algorithm is used to reduce the dimensi...

Polarimetric decomposition and classification are important applications for polarimetric synthetic aperture radar (POLSAR) images. Among many methods developed so far, entropy-anisotropy-alpha classification [1] and three component decomposition [2] are most popular. In an urban area analysis, it is found that the polarimetric analysis has a problem to identify built-up areas. With the three c...

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data clas...

This article presents a new approach for the study of PolSAR images using Time-Frequency (TF) correlation properties . PolSAR information is analyzed using a linear time-frequency (TF) decomposition which permits to describe a scene polarimetric behavior for different azimuth angles of observation and frequencies of illumination. A TF signal model is proposed and studied using two statistical d...

For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to sharp bright-dark variations in intensity. The aggregated terrain type is formulated by the similar ground objects aggregated together. In this paper, a polarimetric hierarchical semantic model (PHSM) is firstly proposed ...

In this paper, we propose a robust unsupervised change detection algorithm for multilook polarimetric synthetic aperture radar (PolSAR) data. The Hotelling-Lawley trace (HLT) statistic is used as a test statistic to measure the similarity of two covariance matrices. The generalized Kittler and Illingworth (K&I) minimum-error thresholding algorithm is then applied on the test statistic image to ...

Based on Time-Frequency (TF) analysis and a-contrario theory, this paper presents a new approach for extraction of linear arranged power transmission tower series in Polarimetric Synthetic Aperture Radar (PolSAR) images. Firstly, the PolSAR multidimensional information is analyzed using a linear TF decomposition approach. The stationarity of each pixel is assessed by testing the maximum likelih...