Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario
Hyperspectral unmixing (HSU) is an essential technique that aims to address the mixed pixels problem in hyperspectral imagery via estimating the abundance of each endmember at every pixel given the endmembers. This article introduces two approaches intending to solve the challenge of the mixed pixel...
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doaj-f589c5ac90f74ce0bbc7a25102a5b3f22021-06-03T23:02:25ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-011356757610.1109/JSTARS.2020.29665128984691Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised ScenarioFarshid Khajehrayeni0Hassan Ghassemian1https://orcid.org/0000-0002-2303-1753Laboratory of Image Processing and Information Analysis, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranLaboratory of Image Processing and Information Analysis, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranHyperspectral unmixing (HSU) is an essential technique that aims to address the mixed pixels problem in hyperspectral imagery via estimating the abundance of each endmember at every pixel given the endmembers. This article introduces two approaches intending to solve the challenge of the mixed pixels using deep convolutional autoencoders (DCAEs), namely pixel-based DCAE, and cube-based DCAE. The former estimates abundances with the help of only spectral information, while the latter utilizes both spectral and spatial information which results in better unmixing performance. In the proposed frameworks, the weights of the decoder are set equal to the end members in order to address the issue in a supervised scenario. The proposed frameworks are also adapted to the VGG-Net that proved increasing depth with small convolution filters (3 × 3) leads to a considerable improvement. In other words, inspired by this idea, we utilize small and fixed kernels of size 3 in all layers of both proposed frameworks. The network is trained via the spectral information divergence objective function, and the dropout and regularization techniques are utilized to prevent overfitting. The superiority of the proposed frameworks is proven via conducting some experiments on both synthetic and real hyperspectral datasets and drawing a comparison with state-ofthe-art methods. Moreover, the quantitative and visual evaluation of the proposed frameworks indicate the necessity of integrating spatial information into the HSU.https://ieeexplore.ieee.org/document/8984691/Deep convolutional autoencoders (DCAEs)hyperspectral unmixing (HSU)spectral-spatial informationVGG-Net |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Farshid Khajehrayeni Hassan Ghassemian |
spellingShingle |
Farshid Khajehrayeni Hassan Ghassemian Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep convolutional autoencoders (DCAEs) hyperspectral unmixing (HSU) spectral-spatial information VGG-Net |
author_facet |
Farshid Khajehrayeni Hassan Ghassemian |
author_sort |
Farshid Khajehrayeni |
title |
Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario |
title_short |
Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario |
title_full |
Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario |
title_fullStr |
Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario |
title_full_unstemmed |
Hyperspectral Unmixing Using Deep Convolutional Autoencoders in a Supervised Scenario |
title_sort |
hyperspectral unmixing using deep convolutional autoencoders in a supervised scenario |
publisher |
IEEE |
series |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
issn |
2151-1535 |
publishDate |
2020-01-01 |
description |
Hyperspectral unmixing (HSU) is an essential technique that aims to address the mixed pixels problem in hyperspectral imagery via estimating the abundance of each endmember at every pixel given the endmembers. This article introduces two approaches intending to solve the challenge of the mixed pixels using deep convolutional autoencoders (DCAEs), namely pixel-based DCAE, and cube-based DCAE. The former estimates abundances with the help of only spectral information, while the latter utilizes both spectral and spatial information which results in better unmixing performance. In the proposed frameworks, the weights of the decoder are set equal to the end members in order to address the issue in a supervised scenario. The proposed frameworks are also adapted to the VGG-Net that proved increasing depth with small convolution filters (3 × 3) leads to a considerable improvement. In other words, inspired by this idea, we utilize small and fixed kernels of size 3 in all layers of both proposed frameworks. The network is trained via the spectral information divergence objective function, and the dropout and regularization techniques are utilized to prevent overfitting. The superiority of the proposed frameworks is proven via conducting some experiments on both synthetic and real hyperspectral datasets and drawing a comparison with state-ofthe-art methods. Moreover, the quantitative and visual evaluation of the proposed frameworks indicate the necessity of integrating spatial information into the HSU. |
topic |
Deep convolutional autoencoders (DCAEs) hyperspectral unmixing (HSU) spectral-spatial information VGG-Net |
url |
https://ieeexplore.ieee.org/document/8984691/ |
work_keys_str_mv |
AT farshidkhajehrayeni hyperspectralunmixingusingdeepconvolutionalautoencodersinasupervisedscenario AT hassanghassemian hyperspectralunmixingusingdeepconvolutionalautoencodersinasupervisedscenario |
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1721398849550942208 |