Compact Polarimetry (CP) can be a very attractive design option for space-borne POLSAR systems, leading to reduced payload power consumption, increased azimuth sampling frequency, increased swath width and potentially reduced data downlink rate demand [2]. However, in order to be able to synthesize target responses for any transmitted and received radar polarisation [3], a process to reconstruc...

Sparse representation-based techniques have shown great potential for pattern recognition problems. Therefore, on the basis of the sparse characteristics of the features for PolSAR image classification, a supervised PolSAR image classification method based on sparse representation is proposed in this paper. It works by projecting the feature vector of the pending pixel onto a subset of training...

In recent years, multi-temporal imagery from spaceborne sensors has provided a fast and practical means for surveying and assessing changes in terrain surfaces. Owing to the all-weather imaging capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection methods include both unsupervised and supervised methods. Supervised change detecti...

This work addresses the problem of covariance matrix estimation for ocean clutter modeling. For ship detection based on polarimetric synthetic aperture radar (PolSAR) imagery and constant false alarm rate (CFAR) detectors, accurate ocean clutter modeling is essential. The covariance matrix provides all the polarimetric information of the ocean clutter and its estimate is always involved in PolS...

We propose a new method for polarimetric synthetic aperture radar (PolSAR) imagery classification based on stochastic distances in the space of random matrices obeying complex Wishart distributions. Given a collection of prototypes [Formula: see text] and a stochastic distance d(.,.), we classify any random matrix X using two criteria in an iterative set-up. First, we associate X with the class...

Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. ...

Segmentation of SAR (Synthetic Aperture Radar) and PolSAR (Polarimetric SAR) images is a challenging task, both because such images are strongly textured and because of the relatively complicated stochastic model that is assumed. Such stochastic models may vary from classical Gaussian to the more advanced SIRVs. Since contour approaches are highly unreliable for textured images, multi-resolutio...

Since the successful launch of ALOS in 2006, we have seen significant advances in the utilization of fully polarimetric SAR (PolSAR) data. We are now in the phase of PolSAR applications for sensing and monitoring the Earth’s environment. Specific application areas include natural and man-made disaster monitoring, forest monitoring, crop assessment, oceanography, etc. In this report, a method of...