NDLI: Effect of pooling strategy on convolutional neural network for classification of hyperspectral remote sensing images

Content Provider	IET Digital Library
Author	Bera, Somenath Shrivastava, Vimal K.
Abstract	The deep convolutional neural network (CNN) has recently attracted the researchers for classification of hyperspectral remote sensing images. The CNN mainly consists of convolution layer, pooling layer and fully connected layer. The pooling is a regularisation technique and improves the performance of CNN while reducing the computation time. Various pooling strategies have been developed in literature. This study shows the effect of pooling strategy on the performance of deep CNN for classification of hyperspectral remote sensing images. The authors have compared the performance of various pooling strategies such as max pooling, average pooling, stochastic pooling, rank-based average pooling and rank-based weighted pooling. The experiments were performed on three well-known hyperspectral remote sensing datasets: Indian Pines, University of Pavia and Kennedy Space Center. The proposed experimental results show that max pooling has produced better results for all the three considered datasets.
Starting Page	480
Ending Page	486
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0561
Journal	IET Image Processing
Publisher Date	2019-10-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Convolution Convolution Layer Deep CNN Deep Convolutional Neural Network Geophysical Image Processing Hyperspectral Remote Sensing Datasets Hyperspectral Remote Sensing Image Image Classification Image Representation Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Max Pooling Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Pooling Layer Pooling Strategy Rank-based Average Pooling Rank-based Weighted Pooling Remote Sensing Statistics Stochastic Pooling
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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