Results 51 to 60 of about 2,421 (185)

Deep learning in remote sensing: a review [PDF]

, 2017
Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich, Mou, Lichao, Tuia, Devis, Xia, Gui-Song, Xu, Feng, Zhang, Liangpei, Zhu, Xiao Xiang   +6 more
core   +4 more sources

Interlinked Dynamics of Rice Cultivation Intensity and Ground Subsidence: Spatio‐Temporal Insights Into Water Resource Implications From Multi‐Year Spaceborne SAR Analysis

Water Resources Research, Volume 62, Issue 4, April 2026.
Abstract Sustainable rice cultivation is vital to meet global food demands, particularly in regions relying on groundwater for irrigation. Over‐pumping, however, can cause significant ground subsidence, threatening both infrastructure and agricultural sustainability.
Ya‐Lun S. Tsai, Xun‐Yan Liu
wiley   +1 more source

Error Source Analysis and Correction of GF-3 Polarimetric Data

Remote Sensing, 2018
The GaoFen-3 (GF-3) satellite is the first polarimetric synthetic aperture radar (PolSAR) satellite in China. With a designed in-orbit life of 8 years, it will provide large amounts of PolSAR data for ocean monitoring, disaster reduction, and many other ...
Sha Jiang, Xiaolan Qiu, Bing Han, Jili Sun, Chibiao Ding   +4 more
doaj   +1 more source

Degree of Polarization-Based Data Filter for Fully Polarimetric Synthetic Aperture Radar [PDF]

, 2019
This paper proposes a novel data filtering algorithm for fully polarimetric synthetic aperture radar (PolSAR) based on the degree of polarization (DoP) information.
Akira Hirose, Fang Shang, Naoto Kishi
core   +2 more sources

Exploring Polarimetric Properties Preservation for PolSAR Image Reconstruction With Complex‐Valued Convolutional Neural Networks

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
Polarimetric SAR data's inherent complex‐valued nature demands algorithms that work directly with complex representations, yet most deep‐learning approaches sidestep this by converting to the real domain. We implement and evaluate complex‐valued convolutional autoencoders that compress and accurately reconstruct full‐polarimetric SAR signals—preserving
Quentin Gabot, Joana Frontera‐Pons, Jérémy Fix, Chengfang Ren, Jean‐Philippe Ovarlez   +4 more
wiley   +1 more source

Entropy-based Statistical Analysis of PolSAR Data

, 2012
Images obtained from coherent illumination processes are contaminated with speckle noise, with polarimetric synthetic aperture radar (PolSAR) imagery as a prominent example.
Cintra, Renato J., Frery, Alejandro C., Nascimento, Abraão D. C.   +2 more
core   +1 more source

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, Cathleen E. Jones, dos Santos, Elijah Ramsey III, Jones, Lee, Lee, Ramsey, Ramsey, Ramsey, Yukihiro Suzuoki   +10 more
core   +2 more sources

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

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

Radar Image Colorization: Converting Single-Polarization to Fully Polarimetric Using Deep Neural Networks

IEEE Access, 2018
Numerous radar polarimetry theories and polarimetric synthetic aperture radar (PolSAR) processing methods have been developed. However, the vast majority of SAR images are not fully polarimetric (full-pol).
Qian Song, Feng Xu, Ya-Qiu Jin
doaj   +1 more source