Extreme Learning Machine With Enhanced Composite Feature for Spectral-Spatial Hyperspectral Image Classification

Jiang, Mengying; Cao, Faxian; Lu, Yunmeng

doi:10.1109/access.2018.2825978

Cited by 25 publications

(11 citation statements)

References 42 publications

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“…The third method compared against is based on the extreme learning machine (ELM) formulation [38]. This is a state-ofthe-art technique known for its very short training time.…”

Section: B Comparison With Shallow Techniquesmentioning

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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“…The third method compared against is based on the extreme learning machine (ELM) formulation [38]. This is a state-ofthe-art technique known for its very short training time.…”

Section: B Comparison With Shallow Techniquesmentioning

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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“…On the other hand, support vector machines [11], kernel-based algorithms [12] and extreme learning machine (ELM) [13][14] have been proved to be very useful for the hyperspectral image classification. Multiple composite features and the composite features have been used to improved the classification accuracy [15]. Also, some sampling query strategies have been proposed to address the limited availability of training samples, such as semi-supervised and active learning methods [16].…”

Section: Introductionmentioning

confidence: 99%

“…During the training process of traditional SLFNs, all the weights and biases need to be tuned iteratively, which is usually solved by gradient-based iterative techniques, e.g.,back-propagation (BP) algorithm [17]. Compared with the traditional BP neural network and support vector machine, the advantages of ELM are fast computation, few parameters, better recognition efficiency and generalization ability [15].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Using Comprehensive Evaluation Model of Extreme Learning Machine Based on Cumulative Variation Weights

Yin

Lin

2020

IEEE Access

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In order to improve the classification of hyperspectral image(HSI), we propose a novel hyperspectral image classification method based on the comprehensive evaluation model of extreme learning machine(ELM) with the cumulative variation weights(CVW), referred to as ELM with the cumulative variation weights and comprehensive evaluation (CVW-CEELM). To be specific, the cumulative variation value is proposed as a new metric. The inefficient bands are eliminated by the cumulative variation quotient values based on the cumulative variation values. The cumulative variation weights based on the cumulative variation values are used to determine the contribution of each weak ELM classifier to the hyperspectral image classification algorithm. The remaining effective bands are divided by grouping strategy. In each group of the effective bands, the different numbers of bands are selected to reduce the dimension of the hyperspectral image dataset by the weighted random-selecting-based method. After dimensionality reduction, the spatial-spectral features of each pixel are extracted and multiple weak ELM classifiers are trained by the training samples. Then, the results of several weak classifiers are synthetically evaluated by the cumulative variation weights to get the final classification results. Experimental results on the typical hyperspectral image datasets illustrate that the proposed CVW-CEELM has few adjustable parameters to make the operation simple, and outperforms a variety of the image classification counterparts in terms of the calculation cost and classification accuracy.

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“…Hyperspectral (HS) images with tens or hundreds of spectral bands can provide abundant spectral information, and have been widely used in environment monitoring [1] [2], image classification [3] [4], target detection [5] [6] and so on. However, the imaging process to achieve high spectral resolution is at the expense of spatial resolution [7].…”

Section: Introductionmentioning

confidence: 99%

Spectral Super-resolution for RGB Images using Class-based BP Neural Networks

Han

Xue

et al. 2018

2018 Digital Image Computing: Techniques and Applications (DICTA)

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Hyperspectral images are of high spectral resolution and have been widely used in many applications, but the imaging process to achieve high spectral resolution is at the expense of spatial resolution. This paper aims to construct a high-spatial-resolution hyperspectral (HHS) image from a highspatial-resolution RGB image, by proposing a novel class-based spectral super-resolution method. With the help of a set of RGB and HHS image-pairs, our proposed method learns nonlinear spectral mappings between RGB and HHS image-pairs using class-based back propagation neural networks (BPNNs). In the training stage, unsupervised clustering is used to divide an RGB image into several classes according to spectral correlation, and the spectrum-pairs from the classified RGB images and the corresponding HHS images are used to train the BPNNs, to establish the nonlinear spectral mapping for each class. In the spectral super-resolution stage, a supervised classification is used to classify the given RGB image into the classes determined during the training stage, and the final HHS image is reconstructed from the classified given RGB image using the trained BPNNs. Comparisons on three standard datasets, ICVL, CAVE and NUS, demonstrate that, our proposed method achieves a better spectral super-resolution quality than related state-of-the-art methods.

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Extreme Learning Machine With Enhanced Composite Feature for Spectral-Spatial Hyperspectral Image Classification

Cited by 25 publications

References 42 publications

Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification

Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification

Hyperspectral Image Classification Using Comprehensive Evaluation Model of Extreme Learning Machine Based on Cumulative Variation Weights

Spectral Super-resolution for RGB Images using Class-based BP Neural Networks

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