Open AccessResearch on SVM Remote Sensing Image Classification Based on Parallelization
Author(s) -
Qi Yang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1852/3/032009
Subject(s) - support vector machine , computer science , structured support vector machine , dimension (graph theory) , pattern recognition (psychology) , artificial intelligence , image (mathematics) , feature (linguistics) , sample (material) , feature vector , contextual image classification , data mining , machine learning , mathematics , linguistics , philosophy , chemistry , chromatography , pure mathematics
With the development of technology, the feature dimension and data volume of remote sensing image classification have increased rapidly. However, when remote sensing image classification based on support vector machine (SVM) is used for large-scale data calculation, there are significant limitations in training time. This paper focuses on the parallel processing method of support vector machine (SVM). Based on the popular hybrid parallel support vector machine, a hybrid parallel support vector machine based on sample cross combination is proposed, and carried on the simulation experiment analysis in the stand-alone environment.