Results 61 to 70 of about 13,122,784 (192)

Multistatic Frequency and Polarimetry Adaptive Autofocus for Synthetic Aperture Radar Imaging of a Moving Target

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
This paper presents a multistatic, frequency‐ and polarimetry‐adaptive autofocus algorithm (F‐Pol) for SAR imaging of moving targets undergoing complex, six‐degrees‐of‐freedom (6‐DoF) motion. Building on Localised Threshold Sharpness (LTS), the method exploits agreement across frequency sub‐bands and polarisation channels to improve pulse‐by‐pulse ...
Anmol Rattan, Daniel Andre, Mark Finnis
wiley   +1 more source

Improved POLSAR Image Classification by the Use of Multi-Feature Combination [PDF]

Remote Sensing, 2015
Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data.
Deng, Lei, Yan, Ya-nan, Wang, Cuizhen
openaire   +2 more sources

Using Texture‐Based Image Segmentation and Machine Learning With High‐Resolution Satellite Imagery to Assess Permafrost Degradation Landforms in the Russian High Arctic

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 3, September 2025.
Abstract Amplified climate change across the Arctic causes significant permafrost thaw and an increase of permafrost degradation landforms. These landforms range from fine‐scale degrading ice wedge‐polygon‐networks to large‐scale features such as thermo‐erosional gullies and reshape entire landscapes.
Cornelia M. Inauen, Ingmar Nitze, Moritz Langer, Anne Morgenstern, Irena Hajnsek, Guido Grosse   +5 more
wiley   +1 more source

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 0001, Hongying Liu 0001, Kun Liu 0011, Yanhe Guo, Licheng Jiao   +5 more
openaire   +2 more sources

Deep Curriculum Learning for PolSAR Image Classification

2022 International Conference on Machine Vision and Image Processing (MVIP), 2022
Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data.
Mousavi, Hamidreza, Imani, Maryam, Ghassemian, Hassan   +2 more
openaire   +2 more sources

CV-CPKAN: Complex-Valued Convolutional Kolmogorov–Arnold Framework for PolSAR Image Classification

Remote Sensing
Deep learning has significantly advanced PolSAR image processing, with a growing trend of integrating mathematical theories into deep neural networks to enhance their capabilities with regard to complex data.
Zuzheng Kuang, Shuxin Liu, Haixia Bi, Lijun He, Fan Li   +4 more
doaj   +1 more source

Aboveground biomass estimation of an old‐growth mangrove forest using airborne LiDAR in the Philippines

Ecological Research, Volume 40, Issue 2, Page 120-132, March 2025.
Using airborne light ranging and light detection (LiDAR) data of the Phil‐LiDAR 1 project, we attempted to develop models to estimate the above‐ground biomass AGB of an old‐growth mangrove forest in the KII Ecopark, Panay Island, Philippines. The common allometric model method showed a large underestimation of AGB for plots with higher canopy heights ...
Mohammad Shamim Hasan Mandal, Rempei Suwa, Rene N. Rollon, Giannina Marie G. Albano, Green Ann A. Cruz, Kenji Ono, Yasmin H. Primavera‐Tirol, Ariel C. Blanco, Kazuo Nadaoka   +8 more
wiley   +1 more source

Permafrost Dynamics Observatory: 3. Remote Sensing Big Data for the Active Layer, Soil Moisture, and Greening and Browning

Earth and Space Science, Volume 12, Issue 1, January 2025.
Abstract Because of the remote nature of permafrost, it is difficult to collect data over large geographic regions using ground surveys. Remote sensing enables us to study permafrost at high resolution and over large areas. The Arctic‐Boreal Vulnerability Experiment's Permafrost Dynamics Observatory (PDO) contains data about permafrost subsidence ...
Elizabeth Wig, Kevin Schaefer, Roger Michaelides, Richard Chen, Leah K. Clayton, Brittany Fager, Lingcao Huang, Andrew D. Parsekian, Howard Zebker, Yingtong Zhang, Yuhuan Zhao   +10 more
wiley   +1 more source

Prototype Theory Based Feature Representation for PolSAR Images

Leida xuebao, 2016
This study presents a new feature representation approach for Polarimetric Synthetic Aperture Radar (PolSAR) image based on prototype theory. First, multiple prototype sets are generated using prototype theory.
Huang Xiaojing, Yang Xiangli, Huang Pingping, Yang Wen   +3 more
doaj   +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