Hyperspectral Image Classification Based on Non-Parallel Support Vector Machine

Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (10), p.2447
Main Authors: Liu, Guangxin, Wang, Liguo, Liu, Danfeng, Fei, Lei, Yang, Jinghui
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Algorithms
Classification
Datasets
Hyperplanes
hyperspectral image
Hyperspectral imaging
Image classification
Neural networks
non-parallel support vector machine
Optimization
Random access memory
Remote sensing
Risk
Risk reduction
Software
support vector machine
Support vector machines
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Summary:Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model adds an additional empirical risk minimization term to the original optimization problem by adding the least square term of the sample and obtains two non-parallel hyperplanes, respectively, forming a new non-parallel SVM algorithm to minimize the additional empirical risk of non-parallel SVM (Additional Empirical Risk Minimization Non-parallel Support Vector Machine, AERM-NPSVM). On the basis of AERM-NPSVM, the bias constraint is added to it, and AERM-NPSVM (BC-AERM-NPSVM) is further obtained. The experimental results show that, compared with the traditional parallel SVM model and the classical non-parallel SVM model, Twin Support Vector Machine (TWSVM), the new model, has a better effect in hyperspectral image classification and better generalization performance.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14102447