Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis
Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into imag...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/9/9/1531 |
id |
doaj-84c3976f4b9b4a84986b0c9a1beb4f32 |
---|---|
record_format |
Article |
spelling |
doaj-84c3976f4b9b4a84986b0c9a1beb4f322020-11-25T03:07:35ZengMDPI AGElectronics2079-92922020-09-0191531153110.3390/electronics9091531Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component AnalysisShanshan Huang0Yikun Yang1Xin Jin2Ya Zhang3Qian Jiang4Shaowen Yao5National Pilot School of Software, Yunnan University, Kunming 650091, ChinaNational Pilot School of Software, Yunnan University, Kunming 650091, ChinaNational Pilot School of Software, Yunnan University, Kunming 650091, ChinaNational Pilot School of Software, Yunnan University, Kunming 650091, ChinaNational Pilot School of Software, Yunnan University, Kunming 650091, ChinaNational Pilot School of Software, Yunnan University, Kunming 650091, ChinaMulti-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method.https://www.mdpi.com/2079-9292/9/9/1531feature extractionmulti-sensor information fusionimage fusionprincipal component analysismultiscale sliding windowssupport vector machine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shanshan Huang Yikun Yang Xin Jin Ya Zhang Qian Jiang Shaowen Yao |
spellingShingle |
Shanshan Huang Yikun Yang Xin Jin Ya Zhang Qian Jiang Shaowen Yao Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis Electronics feature extraction multi-sensor information fusion image fusion principal component analysis multiscale sliding windows support vector machine |
author_facet |
Shanshan Huang Yikun Yang Xin Jin Ya Zhang Qian Jiang Shaowen Yao |
author_sort |
Shanshan Huang |
title |
Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis |
title_short |
Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis |
title_full |
Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis |
title_fullStr |
Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis |
title_full_unstemmed |
Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis |
title_sort |
multi-sensor image fusion using optimized support vector machine and multiscale weighted principal component analysis |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-09-01 |
description |
Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method. |
topic |
feature extraction multi-sensor information fusion image fusion principal component analysis multiscale sliding windows support vector machine |
url |
https://www.mdpi.com/2079-9292/9/9/1531 |
work_keys_str_mv |
AT shanshanhuang multisensorimagefusionusingoptimizedsupportvectormachineandmultiscaleweightedprincipalcomponentanalysis AT yikunyang multisensorimagefusionusingoptimizedsupportvectormachineandmultiscaleweightedprincipalcomponentanalysis AT xinjin multisensorimagefusionusingoptimizedsupportvectormachineandmultiscaleweightedprincipalcomponentanalysis AT yazhang multisensorimagefusionusingoptimizedsupportvectormachineandmultiscaleweightedprincipalcomponentanalysis AT qianjiang multisensorimagefusionusingoptimizedsupportvectormachineandmultiscaleweightedprincipalcomponentanalysis AT shaowenyao multisensorimagefusionusingoptimizedsupportvectormachineandmultiscaleweightedprincipalcomponentanalysis |
_version_ |
1724669621666054144 |