Fu-W:A Hyperspectral Image Classification Algorithm Combining Mini Graph Convolutional Networks and Convolutional neural network
Authors: 
Jiechuan Shi, Kun Liu, Hao Yuan, Can Wang, Bo YangAuthors Info & Claims
Jiechuan Shi, Kun Liu, Hao Yuan, Can Wang, Bo YangAuthors Info & ClaimsPages 377 - 383
Abstract
Convolutional Neural Network (CNN) is a widely used neural network in deep learning, and Graph Convolutional Network (GCN) is one of the most effective semi -supervised methods. It spread node information in a conversion way. In this article, we have studied the differences between CNN and GCN in the classification of high -spectrum image. Because the traditional GCN algorithm needs to build an adjacent matrix on all data, the calculation cost is very high, especially in large -scale remote sensing problems. MinigCNS uses a small -batch learning method to solve the problem of CCN calculation costs, and then it has not solved the problem of low efficiency of single model classification. This article studies the advantages of minigcns and CNN, and proposes a weighted fusion network FU-W, which weighs the minigcns and CNN weighted integration to break the bottleneck of single model performance. We experimented with the fusion algorithm on the two high -spectrum data sets, and its overall accuracy can reach 88.8%in Indian Pines. The experiment proves the superiority of the fusion strategy for a single CNN or GCN model.
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Published In
September 2023
441 pages
ISBN:9798400707667
DOI:10.1145/3627377
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Published: 04 December 2023
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ICBDT 2023
ICBDT 2023: 2023 6th International Conference on Big Data Technologies
September 22 - 24, 2023
Qingdao, China
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