Results 81 to 90 of about 2,397 (207)
Semisupervised PolSAR Image Classification Based on Improved Cotraining
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017 In order to obtain good classification performance of polarimetric synthetic aperture radar (PolSAR) images, many labeled samples are needed for training. However, it is difficult, expensive, and time-consuming to obtain labeled samples in practice. On the other hand, unlabeled samples are substantially cheaper and more plentiful than labeled ones.
Wenqiang Hua +5 more
openaire +1 more source
IET Radar, Sonar &Navigation, Volume 18, Issue 12, Page 2642-2656, December 2024.An optimised scattering power decomposition model is proposed which comprises surface, double‐bounce, oriented dipole and volume scattering components. The authors derive the optimised four‐component decomposition model from mathematical and theoretical perspectives, and verify the rationality of the optimised decomposition model using large amounts of
Lu Fang, Wenxing Mu, Ning Wang, Tao Liu
wiley +1 more source
IET Radar, Sonar &Navigation, Volume 18, Issue 12, Page 2681-2697, December 2024.SAR GMTI is of great importance for both civlisation and military applications. The clutter suppression performance is an important assurance for the accuracy and precision of GMTI. To achieve better clutter suppression performance, it often requires extremely precise registration of multi‐channel data, including polarization and interferometric ...
Jing Xu +3 more
wiley +1 more source
Polarimetric Contextual Classification of PolSAR Images Using Sparse Representation and Superpixels
Remote Sensing, 2014 In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse ...
Jilan Feng, Zongjie Cao, Yiming Pi
doaj +1 more source
Remote Sensing, 2020 Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data.
Lei Wang +4 more
doaj +1 more source
A Non-Parametric Texture Descriptor for Polarimetric SAR Data with Applications to Supervised Classification [PDF]
, 2014 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 ...
Jäger, Marc, Reigber, Andreas
core
Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR) [PDF]
, 2011 The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico.
Amina Rangoonwala +10 more
core +2 more sources
Representative Learning via Span-Based Mutual Information for PolSAR Image Classification [PDF]
, 2021 Jianlong Wang +3 more
openalex +1 more source
Artificial Intelligence applications in Noise Radar Technology
IET Radar, Sonar &Navigation, Volume 18, Issue 7, Page 986-1001, July 2024.Abstract Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application.
Afonso L. Sénica +2 more
wiley +1 more source
IET Radar, Sonar &Navigation, Volume 18, Issue 7, Page 1106-1115, July 2024.The manuscript mainly investigates the sparse distributed vegetation height inversion problem. By analysing the scattering mechanisms of the sparse distributed vegetation, the authors proposed a method to select the samples to estimate PolInSAR coherence and vegetation height in non‐local areas by using the amplitude‐normalised interferometric phase ...
Jing Xu +3 more
wiley +1 more source