R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method

Pan, Bin; Xu, Xia

doi:10.1109/jstars.2017.2655516

Cited by 162 publications

(69 citation statements)

References 35 publications

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“…Furthermore in [37], there is a new model named as RVCANet designed in the context of deep learning for classification of hyperspectral imaging. R-VCANet model is a combination of Rolling Guidance Filter and Vertex Component Analysis Network.…”

Section: Discussionmentioning

confidence: 99%

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The Role of Hyperspectral Imaging: A Literature Review

Mateen¹,

Wen²,

Nasrullah³

et al. 2018

ijacsa

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Abstract-Optical analysis techniques are used recently to detect and identify the objects from a large scale of images. Hyperspectral imaging technique is also one of them. Vision of human eye is based on three basic color (red, green and blue) bands, but spectral imaging divides the vision into many more bands. Hyperspectral remote sensors achieve imagery data in the form of hundreds of adjoining spectral bands. In this paper, our purpose is to illustrate the fundamental concept, hyperspectral remote sensing, remotely sensed information, methods for hyperspectral imaging and applications based on hyperspectral imaging. Moreover, in the forensic context, the novel methods involving deep neural networks are elaborated in this paper. The proposed idea can be useful for further research in the field of hyperspectral imaging using deep learning.

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Section: Discussionmentioning

confidence: 99%

“…R-VCANet is based on natural characteristics of HIS data, spectral properties, and spatial information. Hence the method proposed in [37] has performed better for hyperspectral image classification, especially when the sampling labels are limited.…”

Section: Discussionmentioning

confidence: 99%

The Role of Hyperspectral Imaging: A Literature Review

Mateen¹,

Wen²,

Nasrullah³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…Moreover, since we need sub-feature sets from spectral features, we must extend the pixels spectra to a group of features. Motivated by the effectiveness of RGF and its improvement in HSI classification [37], in this paper, we use RGF to obtain the sub-feature set using spectral information.…”

Section: Rgfmentioning

confidence: 99%

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

Pan

Shi

et al. 2017

Remote Sensing

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Abstract:Integrating spectral and spatial information is proved effective in improving the accuracy of hyperspectral imagery classification. In recent studies, two kinds of approaches are widely investigated: (1) developing a multiple feature fusion (MFF) strategy; and (2) designing a powerful spectral-spatial feature extraction (FE) algorithm. In this paper, we combine the advantages of MFF and FE, and propose an ensemble based feature representation method for hyperspectral imagery classification, which aims at generating a hierarchical feature representation for the original hyperspectral data. The proposed method is composed of three cascaded layers: firstly, multiple features, including local, global and spectral, are extracted from the hyperspectral data. Next, a new hashing based feature representation method is proposed and conducted on the features obtained in the first layer. Finally, a simple but efficient extreme learning machine classifier is employed to get the classification results. To some extent, the proposed method is a combination of MFF and FE: instead of feature fusion or single feature extraction, we use an ensemble strategy to provide a hierarchical feature representation for the hyperspectral data. In the experiments, we select two popular and one challenging hyperspectral data sets for evaluation, and six recently proposed methods are compared. The proposed method achieves respectively 89.55%, 99.36% and 77.90% overall accuracies in the three data sets with 20 training samples per class. The results prove that the performance of the proposed method is superior to some MFF and FE based ones.

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“…Li Wei et al, proposed hyperspectral image classification using deep pixel-pair features [1]. Bin Pan et al, proposed a kind of vertex component analysis network that achieved better performance than some state-of-the-art methods [40].…”

Section: Introductionmentioning

confidence: 99%

Onboard Spectral and Spatial Cloud Detection for Hyperspectral Remote Sensing Images

Zheng

Han

et al. 2018

Remote Sensing

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Abstract:The accurate onboard detection of clouds in hyperspectral images before lossless compression is beneficial. However, conventional onboard cloud detection methods are not applicable all the time, especially for shadowed clouds or darkened snow-covered surfaces that are not identified in normalized difference snow index (NDSI) tests. In this paper, we propose a new spectral-spatial classification strategy to enhance the performance of an orbiting cloud screen obtained on hyperspectral images by integrating a threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and dynamic stochastic resonance (DSR). TESAM is applied to roughly classify cloud pixels based on spectral information. Then aMRF is used to do optimal process by using spatial information, which improved the classification performance significantly. Nevertheless, misclassifications occur due to noisy data in the onboard environments, and DSR is employed to eliminate noise data produced by aMRF in binary labeled images. We used level 0.5 data from Hyperion as a dataset, and the average tested accuracy of the proposed algorithm was 96.28% by test. This method can provide cloud mask for the on-going EO-1 and related satellites with the same spectral settings without manual intervention. Experiments indicate that the proposed method has better performance than the conventional onboard cloud detection methods or current state-of-the-art hyperspectral classification methods.

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R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method

Cited by 162 publications

References 35 publications

The Role of Hyperspectral Imaging: A Literature Review

The Role of Hyperspectral Imaging: A Literature Review

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

Onboard Spectral and Spatial Cloud Detection for Hyperspectral Remote Sensing Images

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