Results 81 to 90 of about 13,122,784 (192)

Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification

Remote Sensing, 2020
Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification.
Yuwei Guo, Zhuangzhuang Sun, Rong Qu, Licheng Jiao, Fang Liu, Xiangrong Zhang   +5 more
doaj   +1 more source

Artificial Intelligence applications in Noise Radar Technology

IET Radar, Sonar &Navigation, Volume 18, Issue 7, Page 986-1001, July 2024.
Abstract Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application.
Afonso L. Sénica, Paulo A. C. Marques, Mário A. T. Figueiredo   +2 more
wiley   +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

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

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

Pearl Millet Crop Biophysical Parameter Retrieval From Space Borne Polarimetric SAR Data Using Machine Learning

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, Dipanwita Haldar, Shashi Kumar, Arun Balaji Ramathilagam, N. R. Patel   +4 more
wiley   +1 more source

PolSAR Scene Classification via Low-Rank Constrained Multimodal Tensor Representation

, 2022
Polarimetric synthetic aperture radar (PolSAR) data can be acquired at all times and are not impacted by weather conditions. They can efficiently capture geometrical and geographical structures on the ground.
Danfeng Hong, Jocelyn Chanussot, Bo Ren, Licheng Jiao, Shibin Ma, Mengqian Chen, Biao Hou   +6 more
core   +1 more source

Context-Based Max-Margin for PolSAR Image Classification

IEEE Access, 2017
Context-based method for classification has been successfully applied in image. However, most of these classifiers work in stages. This paper presents a novel discriminative model named context-based max-margin to perform the task of classification for polarimetric synthetic aperture radar (PolSAR) images.
Shuyin Zhang, Biao Hou, Licheng Jiao, Qian Wu, Chen Sun, Wen Xie 0007   +5 more
openaire   +3 more sources

Entropy/anisotropy/alpha based 3DGabor filter bank for PolSAR image classification

, 2022
There are two types of features in a polarimetric synthetic aperture radar (PolSAR) image: 1-physical scattering characteristics of radar, and 2-geometric and texture properties.
Maryam Imani
core   +1 more source

Global Polarimetric Synthetic Aperture Radar Image Segmentation with Data Augmentation and Hybrid Architecture Model

Remote Sensing
Machine learning and deep neural networks have shown satisfactory performance in the supervised classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, the PolSAR image classification task still faces some challenges. First, the
Zehua Wang, Zezhong Wang, Xiaolan Qiu, Zhe Zhang   +3 more
doaj   +1 more source