Broad Learning System with Locality Sensitive Discriminant Analysis for Hyperspectral Image Classification

Yao, Hongxun; Wei, Yantao; Tian, Yuan

doi:10.1155/2020/8478016

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…However, these spectral bands are closely correlated and incorporate considerable redundant information due to a huge volume of the raw spectral bands and the spatial resolution, henceforward, the difficulty in discriminating the land-cover classes [ 47 ]. Additionally, the key enigma entails extracting the discriminative features of the HSI data to reduce the set of important bands [ 48 ]. In a different outline, the HSI data generally takes a 3D cube form.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

Nyabuga

Song

et al. 2021

Computational Intelligence and Neuroscience

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As one of the fast evolution of remote sensing and spectral imagery techniques, hyperspectral image (HSI) classification has attracted considerable attention in various fields, including land survey, resource monitoring, and among others. Nonetheless, due to a lack of distinctiveness in the hyperspectral pixels of separate classes, there is a recurrent inseparability obstacle in the primary space. Additionally, an open challenge stems from examining efficient techniques that can speedily classify and interpret the spectral-spatial data bands within a more precise computational time. Hence, in this work, we propose a 3D-2D convolutional neural network and transfer learning model where the early layers of the model exploit 3D convolutions to modeling spectral-spatial information. On top of it are 2D convolutional layers to handle semantic abstraction mainly. Toward simplicity and a highly modularized network for image classification, we leverage the ResNeXt-50 block for our model. Furthermore, improving the separability among classes and balance of the interclass and intraclass criteria, we engaged principal component analysis (PCA) for the best orthogonal vectors for representing information from HSIs before feeding to the network. The experimental result shows that our model can efficiently improve the hyperspectral imagery classification, including an instantaneous representation of the spectral-spatial information. Our model evaluation on five publicly available hyperspectral datasets, Indian Pines (IP), Pavia University Scene (PU), Salinas Scene (SA), Botswana (BS), and Kennedy Space Center (KSC), was performed with a high classification accuracy of 99.85%, 99.98%, 100%, 99.82%, and 99.71%, respectively. Quantitative results demonstrated that it outperformed several state-of-the-arts (SOTA), deep neural network-based approaches, and standard classifiers. Thus, it has provided more insight into hyperspectral image classification.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

Nyabuga

Song

et al. 2021

Computational Intelligence and Neuroscience

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“…Only the connection weights between the input layer and the output layer need to be calculated, which greatly improves the training speed of the model. Recently, BLS has been widely used in HSI classification [16][17][18][19][20][21] once it was proposed. Ma et al [17] proposed a novel Multiscale Random Convolution Broad Learning System (MRC-BLS).…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [19] proposed a domain-adaptive and manifold-regularized output layer in the Local Adaptive Broad Learning System, ensuring the effectiveness of the classification results. Yao et al [20] applied local sensitivity discriminant analysis and broad learning to HSI classification.…”

Section: Introductionmentioning

confidence: 99%

Local Discriminative Embedding Broad Learning System With Graph Convolutional for Hyperspectral Image Classification

Wei

Shi

et al. 2023

IEEE Access

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Broad learning system has attracted increasing attention in the hyperspectral image (HSI) classification, due to its universal approximation capability and high efficiency. However, two main challenges remained. One is that the mapping from the original BLS input to the mapped feature (MF) is linear, which is difficult to fully represent the complex spatial-spectral features of HSI. The other is that BLS fails to explore the local geometric structure relationship between samples within HSI. To overcome the limitations mentioned above, we propose a local discriminative embedding broad learning system with graph convolutional (GDEBLS). To address the first challenge, GDEBLS utilizes the graph convolution operation to aggregate the node information in the adjacent graph to learn the context and obtain the rich nonlinear spatial-spectral features in HSI. To deal with the second challenge, our method utilizes a neighborhood selection approach based on manifold structure to calculate the true distances between samples in the manifold space, overcoming the limitations of Euclidean distance measurement. Next, We introduce local manifold structure and discriminative information into BLS. The experimental results show that the proposed method significantly surpasses other state-of-the-art methods.INDEX TERMS broad learning system; hyperspectral image; local geometric structure; graph convolutional.

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“…Researchers have paid a great deal of work to build various classifiers for improving the classification accuracy of HSIs [43], such as random forests [44], neural networks [45], support vector machines (SVM) [46,47], and deep learning [48], reinforcement learning [49], and broad learning systems [50]. Among these classifiers, the BLS classifier [51,52] has attached more and more research attention due to the advantage of its simple structure, few training parameters, and fast training process. Ye et al [53] proposed a novel regularization deep cascade broad learning system (DCBLS) method to apply to the largescale data.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System

Zhao

Wang

et al. 2021

Remote Sensing

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At present many researchers pay attention to a combination of spectral features and spatial features to enhance hyperspectral image (HSI) classification accuracy. However, the spatial features in some methods are utilized insufficiently. In order to further improve the performance of HSI classification, the spectral-spatial joint classification of HSI based on the broad learning system (BLS) (SSBLS) method was proposed in this paper; it consists of three parts. Firstly, the Gaussian filter is adopted to smooth each band of the original spectra based on the spatial information to remove the noise. Secondly, the test sample’s labels can be obtained using the optimal BLS classification model trained with the spectral features smoothed by the Gaussian filter. At last, the guided filter is performed to correct the BLS classification results based on the spatial contextual information for improving the classification accuracy. Experiment results on the three real HSI datasets demonstrate that the mean overall accuracies (OAs) of ten experiments are 99.83% on the Indian Pines dataset, 99.96% on the Salinas dataset, and 99.49% on the Pavia University dataset. Compared with other methods, the proposed method in the paper has the best performance.

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Broad Learning System with Locality Sensitive Discriminant Analysis for Hyperspectral Image Classification

Cited by 7 publications

References 44 publications

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

[Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification

Local Discriminative Embedding Broad Learning System With Graph Convolutional for Hyperspectral Image Classification

Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System

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