A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery

Tan, Kun; Wu, Fuyu; Du, Qian; Du, Peijun; Chen, Yu

doi:10.1109/jstars.2019.2892975

Cited by 48 publications

(26 citation statements)

References 26 publications

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“…We compared the proposed method with SVM [15] and several state-of-the-art methods: 3D-CNN [22], ResNet [24], SSRN [31], DFFN [32], and MPRN [33].…”

Section: Classification Results Of Hyperspectral Datasetsmentioning

confidence: 99%

“…As a typical deep learning model, the stacked autoencoder (SAE) [19,20] can extract both spatial and spectral information and then fuse them for HSI classification. Deep belief networks (DBN) [21] and restricted Boltzmann machines [22] have been proposed for combining spatial information and spectral information of HSIs. However, all of the above methods use one-dimensional feature vectors as the input and do not fully utilize the spatial features in HSIs.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification

Guo

Liu

2020

Remote Sensing

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Extracting spatial and spectral features through deep neural networks has become an effective means of classification of hyperspectral images. However, most networks rarely consider the extraction of multi-scale spatial features and cannot fully integrate spatial and spectral features. In order to solve these problems, this paper proposes a multi-scale and multi-level spectral-spatial feature fusion network (MSSN) for hyperspectral image classification. The network uses the original 3D cube as input data and does not need to use feature engineering. In the MSSN, using different scale neighborhood blocks as the input of the network, the spectral-spatial features of different scales can be effectively extracted. The proposed 3D–2D alternating residual block combines the spectral features extracted by the three-dimensional convolutional neural network (3D-CNN) with the spatial features extracted by the two-dimensional convolutional neural network (2D-CNN). It not only achieves the fusion of spectral features and spatial features but also achieves the fusion of high-level features and low-level features. Experimental results on four hyperspectral datasets show that this method is superior to several state-of-the-art classification methods for hyperspectral images.

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“…We compared the proposed method with SVM [15] and several state-of-the-art methods: 3D-CNN [22], ResNet [24], SSRN [31], DFFN [32], and MPRN [33].…”

Section: Classification Results Of Hyperspectral Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification

Guo

Liu

2020

Remote Sensing

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“…In the case of a great number of samples, unsupervised learning method gradually becomes an operational approach to machine learning. RBM is an unsupervised mapping learning method, which includes the input layer and hidden layer, and the connection between them is a full connection [ 23 ]. There is a connection weight between any two nodes in RBM.…”

Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

Wang

Gao

et al. 2020

Computational Intelligence and Neuroscience

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To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.

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“…The average GS of all the feature bands are used to determine the best image segmentation scale, where the optimal segmentation scale is identified as the one with the lowest average GS value. For the experimental data, the segmentation scales of three datasets are set to [30,35,40,45,50], [25,30,35,40,45] and [25,30,35,40,45], respectively. The results on different segmentation scale are shown in Figure 6.…”

Section: Multi-resolution Segmentationmentioning

confidence: 99%

A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

et al. 2020

Self Cite

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Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.

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A Parallel Gaussian–Bernoulli Restricted Boltzmann Machine for Mining Area Classification With Hyperspectral Imagery

Cited by 48 publications

References 26 publications

A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification

A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

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