Semisupervised Classification for Hyperspectral Images Using Graph Attention Networks

2021 ◽  
Vol 18 (1) ◽  
pp. 157-161
Author(s):  
Anshu Sha ◽  
Bin Wang ◽  
Xiaofeng Wu ◽  
Liming Zhang
Keyword(s):  
Hyperspectral Images ◽  
Attention Networks ◽  
Semisupervised Classification

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity

2017 ◽  
Vol 11 (04) ◽  
pp. 1 ◽  
Author(s):  
Ying Cui ◽  
Guojiao Song ◽  
Xueting Wang ◽  
Zhongjun Lu ◽  
Liguo Wang
Keyword(s):  
Hyperspectral Images ◽  
Training Algorithm ◽  
Semisupervised Classification

A 3D attention networks for classification of white blood cells from microscopy hyperspectral images

2021 ◽  
Vol 139 ◽  
pp. 106931
Author(s):  
Qian Wang ◽  
Jianbiao Wang ◽  
Mei Zhou ◽  
Qingli Li ◽  
Ying Wen ◽  
...  
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Hyperspectral Images ◽  
Attention Networks ◽  
3D Attention

Pre-Processing of Hyperspectral Images Using Nonlinear Filters

2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Edge Preserving ◽  
Improvement Factor ◽  
Filter Output ◽  
Average Improvement ◽  
Processing Procedure

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

Multi-temporal Hyperspectral Images Unmixing and Classification Based on 3D Signature Model and Matching

PIERS Online ◽  
2010 ◽  
Vol 6 (5) ◽  
pp. 480-484 ◽  
Author(s):  
Imed Riadh Farah ◽  
Selim Hemissi ◽  
Karim Saheb Ettabaa ◽  
Bassel Souleiman
Keyword(s):  
Hyperspectral Images ◽  
Multi Temporal
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