Results 81 to 90 of about 2,015 (220)

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, Li, Heng-Chao, Liao, Wenzhi, Liu, Chi, Philips, Wilfried   +4 more
core   +2 more sources

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, Shuang Wang, Hongying Liu, Kun Liu, Yanhe Guo, Licheng Jiao   +5 more
openaire   +1 more source

Sparse vegetation height estimation based on non‐local sample selection with generalised inner product

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, Long Cheng, Chao Xue, Zhiyong Suo   +3 more
wiley   +1 more source

Machine learning classification based on k-Nearest Neighbors for PolSAR data

Anais da Academia Brasileira de Ciências
In this work, we focus on obtaining insights of the performances of some well-known machine learning image classification techniques (k-NN, Support Vector Machine, randomized decision tree and one based on stochastic distances) for PolSAR (Polarimetric ...
JODAVID A. FERREIRA, ANNY K.G. RODRIGUES, RAYDONAL OSPINA, LUIS GOMEZ   +3 more
doaj   +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

Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

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, Xin Xu, Rong Gui, Rui Yang, Fangling Pu   +4 more
doaj   +1 more source

Assessment of Pre‐ and Post‐Fire Fuel Availability for Wildfire Management Based on L‐Band Polarimetric SAR

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

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, Liao, Wenzhi, Liu, Chi, Philips, Wilfried, Wang, Rui   +4 more
core   +1 more source

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

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

Iterative Bilateral Filtering of Polarimetric SAR Data

, 2013
In this paper, we introduce an iterative speckle filtering method for polarimetric SAR (PolSAR) images based on the bilateral filter. To locally adapt to the spatial structure of images, this filter relies on pixel similarities in both spatial and ...
D'Hondt, Olivier, Guillaso, Stéphane, Hellwich, Olaf   +2 more
core   +1 more source