Spectral-spatial classification of hyperspectral image using Distributed Extreme Learning Machine with MapReduce

Shi, Jinmei; Ku, Junhua

doi:10.1109/icbda.2017.8078729

Cited by 3 publications

(6 citation statements)

References 14 publications

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“…The studies in [48,104] used the Gabor filter, which is considered to be similar to the human visual system, and it is widely used in general computer vision tasks, not only in CNNs. The Gabor filter is linear, and it is generally used for analyzing textures in images.…”

Section: Gabor Filtermentioning

confidence: 99%

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Convolutional Extreme Learning Machines: A Systematic Review

et al. 2021

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Much work has recently identified the need to combine deep learning with extreme learning in order to strike a performance balance with accuracy, especially in the domain of multimedia applications. When considering this new paradigm—namely, the convolutional extreme learning machine (CELM)—we present a systematic review that investigates alternative deep learning architectures that use the extreme learning machine (ELM) for faster training to solve problems that are based on image analysis. We detail each of the architectures that are found in the literature along with their application scenarios, benchmark datasets, main results, and advantages, and then present the open challenges for CELM. We followed a well-structured methodology and established relevant research questions that guided our findings. Based on 81 primary studies, we found that object recognition is the most common problem that is solved by CELM, and CCN with predefined kernels is the most common CELM architecture proposed in the literature. The results from experiments show that CELM models present good precision, convergence, and computational performance, and they are able to decrease the total processing time that is required by the learning process. The results presented in this systematic review are expected to contribute to the research area of CELM, providing a good starting point for dealing with some of the current problems in the analysis of computer vision based on images.

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Section: Gabor Filtermentioning

confidence: 99%

“…Frequency and orientation attributes are used in Gabor filters. Similar to the random filter, the Gabor filter that was used in [48,104] obtained a high capacity to represent the data and it could provide a better generalization of ELM.…”

Section: Gabor Filtermentioning

confidence: 99%

Convolutional Extreme Learning Machines: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The works [104], and [48] use the Gabor filter, which is considered similar to the human visual system and is widely used in general computer vision tasks, not only in CNNs. The Gabor filter is linear, and it is generally used for analyzing textures in images.…”

Section: Gabor Filtermentioning

confidence: 99%

“…The frequency and orientation attributes represent the Gabor filters. Similar to the random filter, the Gabor filter used in [104] and [48] obtained a high capacity for representing the data and could provide a better generalization of ELM.…”

Section: Gabor Filtermentioning

confidence: 99%

“…All these architectures presented better training results than classic machine learning models. [51], [48], [54], [50], [49], [46], and [47] used the Pavia dataset for remote sensing classification. Note that remote sensing approaches use another evaluation metrics such as average accuracy (AA), overall accuracy (OA), and Kappa, as shown in Table 14.…”

Section: Rq 3: Which Are the Main Findings When Applying Celm In Problems Based On Image Analysis?mentioning

confidence: 99%

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Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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RETRACTED ARTICLE: A structured support vector machine for hyperspectral satellite image segmentation and classification based on modified swarm optimization approach

Manju

Prabha²

2019

J Ambient Intell Human Comput

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Spectral-spatial classification of hyperspectral image using Distributed Extreme Learning Machine with MapReduce

Cited by 3 publications

References 14 publications

Convolutional Extreme Learning Machines: A Systematic Review

Convolutional Extreme Learning Machines: A Systematic Review

Convolutional Extreme Learning Machines: A Systematic Review

RETRACTED ARTICLE: A structured support vector machine for hyperspectral satellite image segmentation and classification based on modified swarm optimization approach

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