Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data,...

Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to...

Empirical relationships between field-derived Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) and polarimetric synthetic aperture radar (PolSAR) based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were ac...

A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR) image classification method that incorporates texture features and color features is designed and implemented. This method is based on the framework that conjunctively uses statistical region merging (SRM) for segmentation and support vector machine (SVM) for classification. In the segmentation step, we propose an improve...

In model-based decomposition algorithms using polarimetric synthetic aperture radar (PolSAR) data, urban targets are typically identified based on the existence of strong double-bounced scattering. However, urban targets with large azimuth orientation angles (AOAs) produce strong volumetric scattering that appears similar to scattering characteristics from tree canopies. Due to scattering ambig...

During recent years, Time-Frequency (TF) techniques have been introduced to characterize scene polarimetric behaviours using PolSAR data. In this paper, we apply a TF decomposition approach to the analysis of PolSAR data with different frequency bands and show some polarimetric TF behaviours of various scenes. The PolSAR data may be decomposed in azimuth direction, range direction only and in b...

The paper describes a novel representation of polarimetric SAR (PolSAR) data that is inherently non-parametric and therefore particularly suited for characterising data in which the commonly adopted hypothesis of Gaussian backscatter is not appropriate. The descriptor is also non-local and can capture image structure in terms of the arrangement of edge, ridgeand point-like features, to yield a ...

Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of hig...

Polarimetric SAR (PolSAR) is sensitive to the orientation and characters of object and polarimetry could yield several new descriptive radar target detection parameters and lead to the improvement of radar detection algorithms. Target decomposition theory has been used for information extraction in PolSAR, and it can also explore the phase message in PolSAR data. In this paper, a comparison of ...

Unsupervised change detection of agricultural lands in seasonal and annual periods is necessary for farming activities and yield estimation. Polarimetric Synthetic Aperture Radar (PolSAR) data due to their special characteristics are a powerful source to study temporal behaviour of land cover types. PolSAR data allows building up the powerful observations sensitive to the shape, orientation and...