NDLI: FuSENet: fused squeeze-and-excitation network for spectral-spatial hyperspectral image classification

Content Provider	IET Digital Library
Author	Roy, Swalpa Kumar Dubey, Shiv Ram Chatterjee, Subhrasankar Chaudhuri, Bidyut Baran
Abstract	Deep learning-based approaches have become very prominent in recent years due to its outstanding performance as compared to the hand-extracted feature-based methods. Convolutional neural network (CNN) is a type of deep learning architecture to deal with the image/video data. Residual network and squeeze and excitation network (SENet) are among recent developments in CNN for image classification. However, the performance of SENet depends on the squeeze operation done by global pooling, which sometimes may lead to poor performance. In this study, the authors propose a bilinear fusion mechanism over different types of squeeze operation such as global pooling and max pooling. The excitation operation is performed using the fused output of squeeze operation. They used to model the proposed fused SENet with the residual unit and name it as FuSENet. Here the classification experiments are performed over benchmark hyperspectral image datasets. The experimental results confirm the superiority of the proposed FuSENet method with respect to the state-of-the-art methods. The source code of the complete system is made publicly available at https://github.com/swalpa/FuSENet.
Starting Page	1653
Ending Page	1661
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 8, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1462
Journal	IET Image Processing
Publisher Date	2020-04-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Benchmark Hyperspectral Image Datasets Classification Experiments CNN Computer Vision And Image Processing Technique Convolutional Neural Network Deep Learning Architecture Deep Learning-based Approach Excitation Network Excitation Operation Feature Extraction Fused Output Fused Squeeze FuSENet Method Geophysical Image Processing Global Pooling Hand-extracted Feature-based Method Hyperspectral Image Image Classification Image Recognition Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Knowledge Engineering Technique Learning in AI Max Pooling Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Residual Network SENet Spectral-spatial Hyperspectral Image Classification Squeeze Operation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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