Nonlinear Feature Extraction for Hyperspectral Images

Bakir, Çiğdem

doi:10.18100/ijamec.74610

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Secondly, we discuss 5, 10, 15, 20 and 50 extensional neighborhoods for our dataset generated by us by these feature extraction methods and try to classify ECG data [5]. In ECG data, each data is stated as a sample point.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the linear extraction techniques such as PCA and LDA are also used to compare the classification success with the non-linear techniques [4]. Secondly, we discuss 5, 10, 15, 20 and 50 extensional neighborhoods for our dataset generated by us by these feature extraction methods and try to classify ECG data [5]. In ECG data, each data is stated as a sample point.…”

Section: Introductionmentioning

confidence: 99%

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Classification of ECG Signals By the Neighborhood Feature Extraction Method

Bakir¹

2015

Balkan Journal of Electrical and Computer Engineering

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In this study, non-linear dimension reduction methods were applied to ECG signals and success of such dimension reduction techniques for the classification and segmentation of ECG signals were discussed. Also, segmentation of data through neighbourhood feature extraction (NFE) method were enabled by transiting from high dimensioned space to low dimension space by considering the longitudinal combination of ECG signals. Results classification results of NFE algorithm performed through longitudinal combination and as a newly developed method were compared with classification results of ECG signals obtained through dimension reduction by taking one pixel. Results of NFE dimension reduction technique performed by considering the neighbour pixels, advantage of effect on segmentation of ECG signals were presented at empirical results section and the success of suggested method was indicated. Results obtained by performed study are promising for the studies to be conducted in further period.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of ECG Signals By the Neighborhood Feature Extraction Method

Bakir¹

2015

Balkan Journal of Electrical and Computer Engineering

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ECG signals classification with neighborhood feature extraction method

Bakir

2015

2015 Medical Technologies National Conference (TIPTEKNO)

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Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) Spectroscopy Using Fractal-Based Feature Extraction

Liu

Dong

et al. 2016

Remote Sensing

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Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a novel methodology for feature extraction of soil spectroscopy based on fractal geometry. The spectrum can be divided into multiple segments with different step-window pairs. For each segmented spectral curve, the fractal dimension value was calculated using variation estimators with power indices 0.5, 1.0 and 2.0. Thus, the fractal feature can be generated by multiplying the fractal dimension value with spectral energy. To assess and compare the performance of new generated features, we took advantage of organic soil samples from the large-scale European Land Use/Land Cover Area Frame Survey (LUCAS). Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents. Features generated by a variogram estimator performed better than two other estimators and the principal component analysis (PCA). The estimation results for SOC were coefficient of determination (R 2 ) = 0.85, root mean square error (RMSE) = 56.7 g/kg, the ratio of percent deviation (RPD) = 2.59; for pH: R 2 = 0.82, RMSE = 0.49 g/kg, RPD = 2.31; and for N: R 2 = 0.77, RMSE = 3.01 g/kg, RPD = 2.09. Even better results could be achieved when fractal features were combined with PCA components. Fractal features generated by the proposed method can improve estimation accuracies of soil properties and simultaneously maintain the original spectral curve shape.

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Nonlinear Feature Extraction for Hyperspectral Images

Cited by 3 publications

References 14 publications

Classification of ECG Signals By the Neighborhood Feature Extraction Method

Classification of ECG Signals By the Neighborhood Feature Extraction Method

ECG signals classification with neighborhood feature extraction method

Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) Spectroscopy Using Fractal-Based Feature Extraction

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