Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification

Zhong, Ping; Gong, Zhiqiang; Li, Shutao; Schönlieb, Carola‐Bibiane

doi:10.1109/tgrs.2017.2675902

Cited by 303 publications

(151 citation statements)

References 38 publications

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“…iteratively select good-quality labeled samples as training samples. At the same time, Zhong et al [54] developed an improved DBN model, named diversified DBN, to regularize the pretraining and fine-tuning procedures of DBN, which significantly improved the performance of DBN in terms of classification accuracies. In addition, 1-D CNN [55]- [58], 1-D GAN [46], [59], and RNN [44], [58], [60] were also used to extract spectral features for HSI classification.…”

Section: A Spectral-feature Networkmentioning

confidence: 99%

Deep Learning for Hyperspectral Image Classification: An Overview

Li¹,

Song²,

Fang³

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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1,208

452

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Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework which divides the corresponding works into spectralfeature networks, spatial-feature networks, and spectral-spatialfeature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

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Section: A Spectral-feature Networkmentioning

confidence: 99%

Deep Learning for Hyperspectral Image Classification: An Overview

Li¹,

Song²,

Fang³

et al. 2019

IEEE Trans. Geosci. Remote Sensing

Self Cite

1,208

452

View full text Add to dashboard Cite

show abstract

“…w represents the weights between two layers. For the visible and hidden layers of RBM, the interlayer neurons are fully connected and the inner layer neurons are not connected [28][29][30][31]. For a specific set of (v, h), the energy function of the RBM is defined as:…”

Section: Deep Belief Networkmentioning

confidence: 99%

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Lin

Yan

et al. 2018

Energies

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It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

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“…The formulation is made supervised in [2] by using group-sparsity -a technique proposed in [20]. In [3], a diversifying pre-training technique is used for improving the performance of DBN based deep neural network.…”

Section: A Deep Learning In Hyperspectral Classificationmentioning

confidence: 99%

Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification

Singhal

Majumdar

2018

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

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In recent studies in hyperspectral imaging, biometrics and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other traditional deep learning tools when training data is limited; therefore hyperspectral imaging is one such example that benefits from this framework. Most of the prior studies were based on the unsupervised formulation; and in all cases, the training algorithm was greedy and hence sub-optimal. This is the first work that shows how to learn the deep dictionary learning problem in a joint fashion. Moreover, we propose a new discriminative penalty to the said framework. The third contribution of this work is showing how to incorporate stochastic regularization techniques into the deep dictionary learning framework. Experimental results on hyperspectral image classification shows that the proposed technique excels over all state-of-the-art deep and shallow (traditional) learning based methods published in recent times.

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Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification

Cited by 303 publications

References 38 publications

Deep Learning for Hyperspectral Image Classification: An Overview

Deep Learning for Hyperspectral Image Classification: An Overview

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification

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