Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Mercedes E. Paoletti [verfasserIn] Juan M. Haut [verfasserIn] Javier Plaza [verfasserIn] Antonio Plaza [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	hyperspectral images (HSIs) convolutional neural networks (CNNs) dense convolutions

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 10(2018), 9, p 1454
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:9, p 1454

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs10091454

Katalog-ID:	DOAJ01854844X

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language	English
source	In Remote Sensing 10(2018), 9, p 1454 volume:10 year:2018 number:9, p 1454
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authorswithroles_txt_mv	Mercedes E. Paoletti @@aut@@ Juan M. Haut @@aut@@ Javier Plaza @@aut@@ Antonio Plaza @@aut@@
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author	Mercedes E. Paoletti
spellingShingle	Mercedes E. Paoletti misc hyperspectral images (HSIs) misc convolutional neural networks (CNNs) misc dense convolutions misc Science misc Q Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification
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topic_title	Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification hyperspectral images (HSIs) convolutional neural networks (CNNs) dense convolutions
topic	misc hyperspectral images (HSIs) misc convolutional neural networks (CNNs) misc dense convolutions misc Science misc Q
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title	Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification
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title_sort	deep&dense convolutional neural network for hyperspectral image classification
title_auth	Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification
abstract	Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.
abstractGer	Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.
abstract_unstemmed	Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.
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title_short	Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification
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