Nowadays it is widely recognized that SAR polarimetry (POLSAR) and polarimetric SAR interferometry (POLINSAR) add a valuable contribution to promising applications in many fields of remote sensing. SAR polarimetry uses the information embedded in the polarization state of electromagnetic waves scattered by the target; as an example, this technique has been used for vegetation mapping, classific...

Deep learning and convolutional neural networks (CNN) have been widely applied in polarimetric synthetic aperture radar (PolSAR) image classification, satisfactory results obtained. However, there is one crucial issue that still has not solved. These methods require abundant labeled samples obtaining the of PolSAR images usually time-consuming labor-intensive. To obtain better classification wi...

Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast optical imagery, there are several challenges in extracting beneficial features from PolSAR data. Deep learning (DL) methods provide solutions address feature extraction challenges. The convolutional neural networks...

Accurate and reliable crop classification information is a significant data source for agricultural monitoring food security evaluation research. It well-known that polarimetric synthetic aperture radar (PolSAR) provides ample classification. Moreover, multi-temporal PolSAR can further increase accuracies since the crops show different external forms as they grow up. In this paper, we distingui...

The expensive acquisition of labeled data limits the practical use supervised learning on polarimetric synthetic aperture radar (PolSAR) image analysis. Semi-supervised has attracted considerable attention as it can utilize few and very many unlabeled data. scattering response PolSAR is strongly spatial distribution dependent, which provides rich information about land-cover properties. In this...

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and studies detect damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster convenient detection work. However, the existence of non-buildings obliquely-oriented buildings disaster areas presents challenge for obtaining accurate results only PolSAR data. To...

Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice to identify problematic areas on earthen levees. In this paper, using the entropy (H), alpha angle ( ), and eigenvalue parameters (λ), we implemented several unsu...