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

He, Mingyi; Li, Bo; Chen, Huahui

doi:10.1109/icip.2017.8297014

Cited by 322 publications

(164 citation statements)

References 13 publications

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“…The goal of this algorithm is to obtain the material properties of a single-pixel for the identification of different kinds of ground objects in hyperspectral images. In the three public available hyperspectral datasets [26][27][28][29][30][31], the Indian Pines datasets and Salinas datasets target the farmland of different crops and the natural topography. They are continuous large areas of the same category, and the classification effect will be better by using the method of spatial spectrum combination.…”

Section: Resultsmentioning

confidence: 99%

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A hyperspectral image classification algorithm based on atrous convolution

Zhang

Zheng

Liu

et al. 2019

J Wireless Com Network

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Hyperspectral images not only have high spectral dimension, but the spatial size of datasets containing such kind of images is also small. Aiming at this problem, we design the NG-APC (non-gridding multi-level concatenated Atrous Pyramid Convolution) module based on the combined atrous convolution. By expanding the receptive field of three layers convolution from 7 to 45, the module can obtain a distanced combination of the spectral features of hyperspectral pixels and solve the gridding problem of atrous convolution. In NG-APC module, we construct a 15layer Deep Convolutional Neural Networks (DCNN) model to classify each hyperspectral pixel. Through the experiments on the Pavia University dataset, the model reaches 97.9% accuracy while the parameter amount is only 0.25 M. Compared with other CNN algorithms, our method gets the best OA (Over All Accuracy) and Kappa metrics, at the same time, NG-APC module keeps good performance and high efficiency with smaller number of parameters.

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Section: Resultsmentioning

confidence: 99%

“…In this paper, we compare NG-APC model with the classical SVM, the new 1D-CNN [28,29] 2D-CNN [30], 3D-CNN algorithms [31][32][33][34], and RNN [35]. We refer some of these algorithms by nshaud/DeepHyperX on GitHub.…”

Section: Resultsmentioning

confidence: 99%

A hyperspectral image classification algorithm based on atrous convolution

Zhang

Zheng

Liu

et al. 2019

J Wireless Com Network

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“…Zhu et al [12] presented a generative adversarial network comprised of two parts 1D and 2D convolutions. He et al [13] used five layered multi-scale 3D CNN and Chen et al [14] used logistic regression as final classifier for combined data of LIDAR and HS image. Lee et al [18] proposed a contextual deep CNN by concurrently extracting multiple 3-dimensional local convolutional features with different sizes jointly exploiting spatial and spectral features of HS image.…”

Section: Related Workmentioning

confidence: 99%

3D CNN with Localized Residual Connections for Hyperspectral Image Classification

Dwivedi

Mandal

Yadav

et al. 2020

Communications in Computer and Information Science

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In this paper we propose a novel 3D CNN network with localized residual connections for hyperspectral image classification. Our work chalks a comparative study with the existing methods employed for abstracting deeper features and propose a model which incorporates residual features from multiple stages in the network. The proposed architecture processes individual spatiospectral feature rich cubes from hyperspectral images through 3D convolutional layers. The residual connections result in improved performance due to assimilation of both low-level and high-level features. We conduct experiments over Pavia University and Pavia Center dataset for performance analysis. We compare our method with two recent state-of-the-art methods for hyperspectral image classification method. The proposed network outperforms the existing approaches by a good margin.

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“…72.76%(8) 80.95% (11) 86.62% (12) LFDA 71.09% (24) 76.43% (28) 82.50% (8 Finally, we compared the proposed method with the other state-of-the-art deep learning methods of 1D-CNN, the CNN classifier proposed by Hu et al [7], the five-layer CNN classifier proposed by Mei et al [49], and the M3D-DCNN classifier proposed by He et al [50]. All the methods, were compared under the same experimental settings (number of training samples, patch size, etc.)…”

Section: Rldementioning

confidence: 99%

A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction

Tan

et al. 2019

Remote Sensing

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This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, which are the main problems in the local discriminant embedding (LDE) and local Fisher discriminant analysis (LFDA) methods. An active learning method is then used to select the most useful and informative samples from the candidate set. In the experiments undertaken in this study, the three base classifiers were multinomial logistic regression (MLR), k-nearest neighbor (KNN), and random forest (RF). To confirm the effectiveness of the proposed RLDE method, experiments were conducted on two real hyperspectral datasets (Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS)), and the proposed RLDE tri-training algorithm was compared with its counterparts of tri-training alone, LDE, and LFDA. The experiments confirmed that the proposed approach can effectively improve the classification accuracy for hyperspectral imagery.

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

Cited by 322 publications

References 13 publications

A hyperspectral image classification algorithm based on atrous convolution

A hyperspectral image classification algorithm based on atrous convolution

3D CNN with Localized Residual Connections for Hyperspectral Image Classification

A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction

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