Hyperspectral Image Classification Based on Domain Adaptation Broad Learning

Wang, Haoyu; Wang, Xuesong; Chen, C. L. Philip; Cheng, Yuhu

doi:10.1109/jstars.2020.3001198

Cited by 32 publications

(11 citation statements)

References 43 publications

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“…Recently, Stacked BLS has been proposed to enhance the accuracy rate of models [40]. Moreover, the BLS and its variants [41], [42], [43] have been used in other fields, including image classification [44], [45], industrial process [36], resource utilization [46], medical care [47], time series prediction [48], maximum information network [49], network traffic flow prediction [43], [50], event commentary [51] and as well as traffic forecasting [52]. In the traffic area, the BRL methods have been first adopted in TLC [21].…”

Section: B Broad Learning Systemsmentioning

confidence: 99%

Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control

Zhu¹,

Li²,

Wu³

et al. 2022

Preprint

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Intelligent Traffic Light Control System (ITLCS) is a typical Multi-Agent System (MAS), which comprises multiple roads and traffic lights. Constructing a model of MAS for ITLCS is the basis to alleviate traffic congestion. Existing approaches of MAS are largely based on Multi-Agent Deep Reinforcement Learning (MADRL). Although the Deep Neural Network (DNN) of MABRL is effective, the training time is long, and the parameters are difficult to trace. Recently, Broad Learning Systems (BLS) provided a selective way for learning in the deep neural networks by a flat network. Moreover, Broad Reinforcement Learning (BRL) extends BLS in Single Agent Deep Reinforcement Learning (SADRL) problem with promising results. However, BRL does not focus on the intricate structures and interaction of agents. Motivated by the feature of MADRL and the issue of BRL, we propose a Multi-Agent Broad Reinforcement Learning (MABRL) framework to explore the function of BLS in MAS. Firstly, unlike most existing MADRL approaches, which use a series of deep neural networks structures, we model each agent with broad networks. Then, we introduce a dynamic self-cycling interaction mechanism to confirm the "3W" information: When to interact, Which agents need to consider, What information to transmit. Finally, we do the experiments based on the intelligent traffic light control scenario. We compare the MABRL approach with six different approaches, and experimental results on three datasets verify the effectiveness of MABRL.

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Section: B Broad Learning Systemsmentioning

confidence: 99%

Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control

Zhu¹,

Li²,

Wu³

et al. 2022

Preprint

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“…So HSIs are widely used in the fields of precision agriculture, environmental monitoring, urban planning, and military reconnaissance [1]- [3]. Among these applications, hyperspectral image classification (HSIC) is one of the important links and has attracted more and more attention [4]- [9]. The ultimate This work is supported by the National Natural Science Foundation of China (61772397,12005169), National Key R&D Program of China (2016YFE0200400), the Open Research Fund of Key Laboratory of Digital Earth Science (2019LDE005), and science and technology innovation team of Shaanxi Province (2019TD-002).…”

Section: Introductionmentioning

confidence: 99%

A Pixel Cluster CNN and Spectral-Spatial Fusion Algorithm for Hyperspectral Image Classification With Small-Size Training Samples

Dong

Quan

Feng

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Convolutional neural networks (CNNs) can automatically learn features from the hyperspectral image (HSI) data, avoiding the difficulty of manually extracting features. However, the number of training samples for the classification of HSIs is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. To solve this problem, a pixel cluster CNN and spectral-spatial fusion (PC-CNN-SSF) algorithm for hyperspectral image classification (HSIC) with small-size training samples is proposed in this paper. Firstly, spatial information is extracted by the Gray Level Co-occurrence Matrix (GLCM). Then spatial information and spectral information are fused by means of bands superposition, forming spectral-spatial features. To expand the number of training samples, the pixels after spectral-spatial fusion are combined into pixel clusters according to a certain rule. Finally, a CNN framework is utilized to extract effective features from the pixel clusters. Experiments based on three standard HSIs demonstrate that the proposed algorithm can get better performance than the conventional CNN and also outperforms other studied algorithms in the case of small training set.

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“…The discriminative locality preserving broad learning system (DPBLS) [54] was utilized to capture the manifold structure between neighbor pixels of hyperspectral images. Wang et al [55] proposed the HSI classification method based on domain adaptation broad learning (DABL) to solve the limitation or absence of the available labeled samples. Kong et al [56] proposed a semi-supervised BLS (SBLS).…”

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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Hyperspectral Image Classification Based on Domain Adaptation Broad Learning

Cited by 32 publications

References 43 publications

Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control

Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control

A Pixel Cluster CNN and Spectral-Spatial Fusion Algorithm for Hyperspectral Image Classification With Small-Size Training Samples

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

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