Multi-scale 3D deep convolutional neural network for hyperspectral image classification

Research in deep neural network (DNN) and deep learning has great progress for 1D (speech), 2D (image) and 3D (3D-object) recognition/classification problems. As HSI that with 2D spatial and 1D spectral information is quite different from 3D object image, the existing DNN cannot be directly extended...

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Bibliographic Details
Published in2017 IEEE International Conference on Image Processing (ICIP) pp. 3904 - 3908
Main Authors He, Mingyi, Li, Bo, Chen, Huahui
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
3D convolution
Convolution
deep neural network
end-to-end
Feature extraction
Hyperspectral image classification
Hyperspectral imaging
Kernel
multi-scale
Three-dimensional displays
Training
Two dimensional displays
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Summary:Research in deep neural network (DNN) and deep learning has great progress for 1D (speech), 2D (image) and 3D (3D-object) recognition/classification problems. As HSI that with 2D spatial and 1D spectral information is quite different from 3D object image, the existing DNN cannot be directly extended to hyperspectral image (HSI) classification. A Multiscale 3D deep convolutional neural network (M3D-DCNN) is proposed for HSI classification, which could jointly learn both 2D Multi-scale spatial feature and 1D spectral feature from HSI data in an end-to-end approach, promising to achieve better results with large-scale dataset. Although without any hand-craft features or pre/post-processing like PCA, sparse coding etc, we achieve the state-of-the-art results on the standard datasets, which shows the technical validity and advancement of our method.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8297014