Polarimetric synthetic aperture radar (PolSAR) sensors have reached an essential position in remote sensing. The images they provide speckle noise, making their processing and analysis challenging tasks. We discuss edge detection method based on the fusion of evidences obtained intensity channels hh, hv, vv PolSAR multilook images. consists detecting transition points thinnest possible range da...

Land Use and Cover (LULC) classification is one of the tasks Polarimetric Synthetic Aperture Radar (PolSAR) images’ interpretation, performance existing algorithms highly sensitive to class number, which inconsistent with reality that LULC should have multiple levels detail in same image. Therefore, an object-oriented unsupervised algorithm for PolSAR images based on image block proposed. First...

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On contrary, research works investigating unsupervised PolSAR classification quite rare, because most CNN need be tra...

Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often af...

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the classification accuracy and helps to realize rapid deforestation detection because are more frequently acquired than POLSAR The proposed involves constructing models considering radar scenario ...

We address the issue of adapting optical images-based edge detection techniques for use in Polarimetric Synthetic Aperture Radar (PolSAR) imagery. We modify the gravitational edge detection technique (inspired by the Law of Universal Gravity) proposed by Lopez-Molina et al, using the non-standard neighbourhood configuration proposed by Fu et al, to reduce the speckle noise in polarimetric SAR i...

This paper presents a comparison between two types of initializations for multilook polarimetric SAR image segmentation: a random partition and a sample quantile partition. These are the inputs of a stochastic expectation-maximization algorithm that uses a mixture of G0 P distributions to describe the data. The parameters are unknown, and estimated by the moments method. The G0 P law is able to...

This paper uses RADARSAT-2 quad Polarimetric Synthetic Aperture Radar (PolSAR) and TerraSAR-X dual polarimetric SAR data to monitor agriculture crop growth stages. Two RADARSAT-2 Fine Quad Wide (FQW) beam modes FQ2W and FQ10W, each with 5 sets of data and 13 sets of Stripmap TerraSAR-X data were used in the study. Both RADARSAT-2 POLSAR data and TerraSARX data were acquired in summer 2012 outsi...

This Mangrove forests in the United Arab Emirates (UAE) provide valuable ecosystem services such as coastal erosion protection, water purification and refuge for a wide variety of plants animals. Therefore, first step toward understanding mangrove is monitoring this important ecological system. paper proposes an original study to characterize forest environment UAE by using polarimetric syntheti...

The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the CNN only consider the polarization information of the image, instead of incorporating the image’s spatial information. In this paper, a novel method...