Results 71 to 80 of about 2,377 (186)
Earth and Space Science, Volume 11, Issue 4, April 2024.Abstract Many communities coexist with wildfires that lead to loss of lives, property, and ecosystem services. Remote sensing tools can aid disaster response and post‐event assessment, offering fire agencies opportunities for additional surveillance with radar, an all‐weather instrument that can image day or night.
Karen An, Cathleen E. Jones, Yunling Lou
wiley +1 more source
Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints [PDF]
, 2018 This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints.
Fu, Kun +4 more
core +2 more sources
Semiparametric constant false alarm rate method for radar and sonar images
Electronics Letters, Volume 60, Issue 5, March 2024.This study proposed a novel constant false alarm rate (CFAR) method based on Gaussian mixture model (GMM). The reason for starting this work is that some new polarimetric detectors and the high‐resolution cases may lead to the failure of traditional parametric model.
Ke Li, Peng Zhang, Ziyuan Yang
wiley +1 more source
Polarimetric Incoherent Target Decomposition by Means of Independent Component Analysis [PDF]
, 2015 International audienceThis paper presents an alternative approach for polarimetric incoherent target decomposition dedicated to the analysis of very-high resolution POLSAR images.
Besic, Nikola +3 more
core +3 more sources
IET Radar, Sonar &Navigation, Volume 18, Issue 3, Page 379-409, March 2024.The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. Also, the designed deep learning model can obtain the effective features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self‐supervised manner.
Mohsen Darvishnezhad, Mohammad Ali Sebt
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
Optimal Parameter Estimation in Heterogeneous Clutter for High Resolution Polarimetric SAR Data [PDF]
, 2010 International audienceThis letter presents a new estimation scheme for optimally deriving clutter parameters with high-resolution polarimetric synthetic aperture radar (POLSAR) data.
Formont, Pierre +4 more
core +6 more sources
Earth and Space Science, Volume 11, Issue 1, January 2024.Abstract The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height,
Dharanya Thulasiraman +4 more
wiley +1 more source
A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks [PDF]
, 2018 Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and ...
Adriano, Bruno +6 more
core +1 more source
Semi-supervised classification of polarimetric SAR images using Markov random field and two-level Wishart mixture model [PDF]
, 2019 In this work, we propose a semi-supervised method for classification of polarimetric synthetic aperture radar (PolSAR) images. In the proposed method, a 2-level mixture model is constructed by associating each component density with a unique Wishart ...
Li, Heng-Chao +4 more
core +1 more source