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dc.contributor.author Li, Ying
dc.contributor.author Zhang, Haokui
dc.contributor.author Shen, Qiang
dc.date.accessioned 2017-01-16T23:05:46Z
dc.date.available 2017-01-16T23:05:46Z
dc.date.issued 2017-01-13
dc.identifier.citation Li , Y , Zhang , H & Shen , Q 2017 , ' Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network ' Remote Sensing , vol. 9 , no. 1 , 67 . https://doi.org/10.3390/rs9010067 en
dc.identifier.issn 2072-4292
dc.identifier.other PURE: 10373421
dc.identifier.other PURE UUID: b4481441-a268-44ff-b005-7ecc1d3215c0
dc.identifier.other Scopus: 85010690651
dc.identifier.other handle.net: 2160/44486
dc.identifier.uri http://hdl.handle.net/2160/44486
dc.description.abstract Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record. en
dc.language.iso eng
dc.relation.ispartof Remote Sensing en
dc.rights en
dc.subject hyperspectral image classification en
dc.subject deep learning en
dc.subject 2D convolutional neural networks en
dc.subject 3D convolutional neural networks en
dc.subject 3D structure en
dc.title Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version publishersversion en
dc.identifier.doi https://doi.org/10.3390/rs9010067
dc.contributor.institution Department of Computer Science en
dc.contributor.institution IMPACS en
dc.description.status Peer reviewed en


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