Results 21 to 30 of about 11,244,032 (159)

MCDiff: A Multilevel Conditional Diffusion Model for PolSAR Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
With the swift advancement of deep learning, significant strides have been made in polarimetric synthetic aperture radar (PolSAR) image classification, particularly with the advent of diffusion models that allow for explicit class probability modeling ...
Qingyi Zhang, Xiaoxiao Fang, Tao Liu, Ronghua Wu, Liguo Liu, Chu He   +5 more
doaj   +2 more sources

A 3-D Convolutional Vision Transformer for PolSAR Image Classification and Change Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The scattering properties of targets in polarimetric synthetic aperture radar (PolSAR) images are directly influenced by the targets' orientations, as the scattering properties from the same target with different orientations can be very different.
Lei Wang, Rong Gui, Hanyu Hong, Jun Hu, Lei Ma, Yu Shi   +5 more
doaj   +2 more sources

PCCN: Polarimetric Contexture Convolutional Network for PolSAR Image Super-Resolution

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Polarimetric synthetic aperture radar (PolSAR) can acquire full-polarization information, which is the solid foundation for target scattering mechanism interpretation and utilization. Meanwhile, PolSAR image resolution is usually lower than the synthetic
Lin-Yu Dai, Ming-Dian Li, Si-Wei Chen
doaj   +2 more sources

DESPECKLING POLSAR IMAGES BASED ON RELATIVE TOTAL VARIATION MODEL [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018
Relatively total variation (RTV) algorithm, which can effectively decompose structure information and texture in image, is employed in extracting main structures of the image.
C. Jiang, C. Jiang, X. F. He, L. J. Yang, J. Jiang, D. Y. Wang, Y. Yuan   +6 more
doaj   +1 more source

An active deep learning approach for minimally supervised polsar image classification [PDF]

, 2019
Recently, deep neural networks have received intense interests in polarimetric synthetic aperture radar (PolSAR) image classification. However, its success is subject to the availability of large amounts of annotated data which require great efforts of ...
Xue, Yong
core   +1 more source

Ice Volume Characterization using Long-Wavelength Airborne PolSAR Data [PDF]

, 2012
The interest in studying land ice for glaciological and climate change research has increased in recent years. The need of information on a global scale makes synthetic aperture radar (SAR) suitable for these studies.
Konstantinos P. Papathanassiou, Jagdhuber, Thomas, Papathanassiou, Konstantinos P., Hajnsek, Irena, Parrella, Giuseppe, Al-Kahachi, Noora, Papathanassiou, Kostantinos, Thomas Jagdhuber, Noora Al-Kahachi, Irena Hajnsek, Giuseppe Parrella   +10 more
core   +1 more source

Classification of Polarimetric SAR Images Based on the Riemannian Manifold

Leida xuebao, 2017
Classification is one of the core components in the interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. A new PolSAR image classification approach employs the structural properties of the Riemannian manifold formed by PolSAR ...
Yang Wen, Zhong Neng, Yan Tianheng, Yang Xiangli   +3 more
doaj   +1 more source

A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification

IEEE Access, 2020
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric
Junfei Shi, Haiyan Jin, Xiaohua Li
doaj   +1 more source

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification [PDF]

, 2021
The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which ...
Wang, Shuo, Jiao, Licheng, Sun, Zhuangzhuang, Qu, Rong, Wang, Shuang, Guo, Yuwei, Liu, Fang   +6 more
core   +1 more source

Object-Based Classification of PolSAR Images Based on Spatial and Semantic Features

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
High-resolution polarimatric synthetic aperture radar (PolSAR) images can provide more detail information on land-cover types and increase the image complexity at the same time.
Bin Zou, Xiaofang Xu, Lamei Zhang
doaj   +1 more source