Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification

Hyperspectral image (HSI) classification is a most challenging task in hyperspectral remote sensing field due to unique characteristics of HSI data. It consists of huge number of bands with strong correlations in the spectral and spatial domains. Moreover, limited training samples make it more chall...

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Vydáno v:International journal of remote sensing Ročník 41; číslo 7; s. 2664 - 2683
Hlavní autoři: Bera, Somenath, Shrivastava, Vimal K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 02.04.2020
Taylor & Francis Ltd
Témata:
Artificial neural networks
Classification
data collection
Detection
Domains
Feature extraction
hyperspectral imagery
Hyperspectral imaging
Image classification
Neural networks
Remote sensing
spatial data
Training
ISSN:0143-1161, 1366-5901, 1366-5901
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Shrnutí:Hyperspectral image (HSI) classification is a most challenging task in hyperspectral remote sensing field due to unique characteristics of HSI data. It consists of huge number of bands with strong correlations in the spectral and spatial domains. Moreover, limited training samples make it more challenging. To address such problems, we have presented here a spatial feature extraction technique using deep convolutional neural network (CNN) for HSI classification. As optimizer plays an important role in learning process of deep CNN model, we have presented the effect of seven different optimizers on our deep CNN model in the application of HSI classification. The seven different optimizers used in this study are SGD, Adagrad, Adadelta, RMSprop, Adam, AdaMax, and Nadam. Extensive experimental results on four hyperspectral remote sensing data sets have been presented which demonstrate the superiority of the presented deep CNN model with Adam optimizer for HSI classification.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2019.1694725