Small-sample classification of hyperspectral data in a graph-based semi-supervision framwork

2017 ◽  
Author(s):  
Chunmei Zhang ◽  
Junyan Wang ◽  
Yunbin Zhang ◽  
Yaoyao Liu
Keyword(s):  
Small Sample ◽  
Hyperspectral Data ◽  
Sample Classification

scholarly journals Improved Classification Method Based on the Diverse Density and Sparse Representation Model for a Hyperspectral Image

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5559
Author(s):  
Na Li ◽  
Ruihao Wang ◽  
Huijie Zhao ◽  
Mingcong Wang ◽  
Kewang Deng ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Infrared Imaging ◽  
Hyperspectral Image ◽  
Matching Pursuit ◽  
Small Sample Size ◽  
Small Sample ◽  
Hyperspectral Data ◽  
Classification Method ◽  
Diverse Density

To solve the small sample size (SSS) problem in the classification of hyperspectral image, a novel classification method based on diverse density and sparse representation (NCM_DDSR) is proposed. In the proposed method, the dictionary atoms, which learned from the diverse density model, are used to solve the noise interference problems of spectral features, and an improved matching pursuit model is presented to obtain the sparse coefficients. Airborne hyperspectral data collected by the push-broom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS) are applied to evaluate the performance of the proposed classification method. Results illuminate that the overall accuracies of the proposed model for classification of PHI and AVIRIS images are up to 91.59% and 92.83% respectively. In addition, the kappa coefficients are up to 0.897 and 0.91.

scholarly journals Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 117096-117108
Author(s):  
Bing Liu ◽  
Wenyue Guo ◽  
Xin Chen ◽  
Kuiliang Gao ◽  
Xibing Zuo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hyperspectral Image ◽  
Small Sample ◽  
Sample Classification ◽  
Morphological Attribute

Application on small sample classification of ER rule classifier

2021 ◽  
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao
Keyword(s):  
Small Sample ◽  
Sample Classification

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

2021 ◽  
pp. 1-15
Author(s):  
Phawis Thammasorn ◽  
Wanpracha A. Chaovalitwongse ◽  
Daniel S. Hippe ◽  
Landon S. Wootton ◽  
Eric C. Ford ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Nearest Neighbor ◽  
Small Sample ◽  
Imaging Data ◽  
Medical Imaging Data

Using class-based feature selection for the classification of hyperspectral data

2011 ◽  
Vol 32 (15) ◽  
pp. 4311-4326 ◽  
Author(s):  
Yasser Maghsoudi ◽  
Mohammad Javad Valadan Zoej ◽  
Michael Collins
Keyword(s):  
Feature Selection ◽  
Hyperspectral Data ◽  
Selection For

scholarly journals Classification of multiwavelength transients with Machine Learning

2020 ◽  
Author(s):  
K Sooknunan ◽  
M Lochner ◽  
Bruce A Bassett ◽  
H V Peiris ◽  
R Fender ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Sample ◽  
Light Curves ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Test Time ◽  
Test Accuracy ◽  
Training Set ◽  
The Impact

Abstract With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the eleven classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78%. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97%, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19%.

Sign in / Sign up

Export Citation Format

Share Document