Classification of Hyperspectral Remote Sensing Images by an Ensemble of Support Vector Machines Under Imbalanced Data

Eeti, Laxmi Narayana; Buddhiraju, Krishna Mohan

doi:10.1109/igarss.2018.8519564

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The Support Vector Machine (SVM) algorithm is used to create a model and see the performance of the balancing method used. Based on the literature obtained, this algorithm works quite well with imbalanced data conditions [19], [21].…”

Section: Introductionmentioning

confidence: 89%

Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods

Purwadi,

Abu,

Mohd

et al. 2023

JOIV : Int. J. Inform. Visualization

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Hyperspectral image technology in land classification is a distinct advantage compared to ordinary RGB and multispectral images. This technology has a wide spectrum of electromagnetic waves, which can be more detailed than other types of imagery. Therefore, with its hyperspectral advantages, the characteristics of an object should have a high probability of being recognized and distinguished. However, because of the large data, it becomes a challenge to lighten the computational burden. Hyperspectral has a huge phenomenon that makes computations heavy compared to other types of images because this image is 3D. The problem faced in hyperspectral image classification is the high computational load, especially if the spatial resolution of the image also has mixed pixel problems. This research uses EO-1 satellite imagery with a spatial resolution of 30 meters and a mixed pixel problem. This study uses a classification method to lighten the computational burden and simultaneously increase the value of classification accuracy. The method used is satellite image pre-processing, including geometric correction and image enhancement using FLAASH while the corrections are geometric correction and atmospheric correction. Then to lighten the computational burden, the steps carried out are using the Slab and PCA method. After obtaining the characteristics, they are entered into a guided learning model using a support vector machine (SVM) for the five-class or multiclass classification. Moreover, the imbalance learning method is proven to produce increased accuracy. The best results were achieved by the ADASYN method with an accuracy of 96.58%, while the computational time became faster with the feature extraction method.

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Section: Introductionmentioning

confidence: 89%

Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods

Purwadi,

Abu,

Mohd

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…Support vector machine (SVM) is a classical paradigm for solving supervised learning problem [1]- [3], which holds good theoretical foundation without sacrificing generalization performance in various applications [4]- [6]. Traditional SVM is sensitive to noises and imbalanced data problem, owing to its unbounded convex loss and unified penalty parameter for all samples.…”

Section: Introductionmentioning

confidence: 99%

Robust Rescaled Hinge Loss Twin Support Vector Machine for Imbalanced Noisy Classification

et al. 2019

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Support vector machine (SVM) and twin SVM (TWSVM) are sensitive to the noisy classification, due to the unlimited measures in their losses, especially for imbalanced classification problem. In this paper, by combining the advantages of the correntropy induced loss function (C-Loss) and the hinge loss function (hinge loss), we introduce the rescaled hinge loss function (Rhinge loss), which is a monotonic, bounded, and nonconvex loss, into TWSVM for imbalanced noisy classification, called RTBSVM. We show that the Rhinge loss could approximate the hard margin loss and the hinge loss by adjusting the rescaled parameter, and further, our RTBSVM could improve the stability and performance of TWSVM and it is effective for imbalanced noisy classification. The experimental results show that our method performs better than the compared TWSVMs and robust SVMs on the imbalanced noisy classification.

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Enhanced Support Vector Machine Applied to Land-Use Classification

Elmannai

Hamdi

Algarni

2019

Communications in Computer and Information Science

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Classification of Hyperspectral Remote Sensing Images by an Ensemble of Support Vector Machines Under Imbalanced Data

Cited by 4 publications

References 2 publications

Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods

Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods

Robust Rescaled Hinge Loss Twin Support Vector Machine for Imbalanced Noisy Classification

Enhanced Support Vector Machine Applied to Land-Use Classification

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