Volumetric Directional Pattern for Spatial Feature Extraction in Hyperspectral Imagery

Essa, Almabrok; Sidike, Paheding; Asari, Vijayan K.

doi:10.1109/lgrs.2017.2695559

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…Among machine learning methods, Extreme Learning Machine (ELM; [31]), and its variants with different activation functions, have been successfully applied to a variety of research fields [32][33][34][35][36][37]. The activation function is the nonlinear transformation of the weighted input signals and bias [38].…”

Section: Introductionmentioning

confidence: 99%

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

Maimaitiyiming

Sagan

Kwasniewski³

2019

Remote Sensing

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Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to predict plant traits, there are still limitations to previously studied techniques for vine productivity due to the complexity of the system not being adequately modeled. During the 2014 and 2015 growing seasons, hyperspectral reflectance spectra were collected using a handheld spectroradiometer in a vineyard designed to investigate the effects of irrigation level (0%, 50%, and 100%) and rootstocks (1103 Paulsen, 3309 Couderc, SO4 and Chambourcin) on vine productivity. To assess vine productivity, it is necessary to measure factors related to fruit ripeness and not just yield, as an over cropped vine may produce high-yield but poor-quality fruit. Therefore, yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were measured. A total of 20 vegetation indices were calculated from hyperspectral data and used as input for predictive model calibration. Prediction performance of linear/nonlinear multiple regression methods and Weighted Regularized Extreme Learning Machine (WRELM) were compared with our newly developed WRELM-TanhRe. The developed method is based on two activation functions: hyperbolic tangent (Tanh) and rectified linear unit (ReLU). The results revealed that WRELM and WRELM-TanhRe outperformed the widely used multiple regression methods when model performance was tested with an independent validation dataset. WRELM-TanhRe produced the highest prediction accuracy for all the berry yield and quality parameters (R2 of 0.522–0.682 and RMSE of 2–15%), except for TA, which was predicted best with WRELM (R2 of 0.545 and RMSE of 6%). The results demonstrate the value of combining hyperspectral remote sensing and machine learning methods for improving of berry yield and quality prediction.

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Section: Introductionmentioning

confidence: 99%

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

Maimaitiyiming

Sagan

Kwasniewski³

2019

Remote Sensing

Self Cite

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“…In this section, we present the observation of capturing any small changes of the face textures and merging the movement and appearance features together. Therefore, we deeply explain the essentials of the proposed technique which is our previous work, named volumetric directional pattern (VDP) [14,15]. The main goal of the VDP is extracting and fusing the temporal information (dynamic features) from three consecutive frames which are distinct under multiple poses and facial expressions variations.…”

Section: Related Workmentioning

confidence: 99%

“…Volumetric Directional Pattern. Volumetric directional pattern (VDP) is a gray-scale pattern that characterizes and fuses the temporal structure (dynamic information) of three consecutive frames [14,15]. VDP has been developed to merge the movement and appearance features together.…”

Section: Related Workmentioning

confidence: 99%

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High Order Volumetric Directional Pattern for Video‐Based Face Recognition

Essa

Asari

2019

Mathematical Problems in Engineering

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Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the nth order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches.

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“…To take into account spatial image features at multiple scales, an extension towards the use of multiscale texture features has been presented [15]. Besides, it has been proposed to fuse information from adjacent spectral bands, e.g., by extracting texture features from different direction patterns and thereby not only considering neighboring pixels but also the characteristics across consecutive bands [16].…”

Section: Feature Extractionmentioning

confidence: 99%

Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data

2018

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In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset of generally versatile features is retained. We present different techniques relying on the topology of the given sparse training data. Thereby, the topology is described with an ultrametricity index. For the latter, we take into account the Murtagh Ultrametricity Index (MUI) which is defined on the basis of triangles within the given data and the Topological Ultrametricity Index (TUI) which is defined on the basis of a specific graph structure. In a case study addressing the classification of high-dimensional hyperspectral data based on sparse training data, we demonstrate the performance of the proposed unsupervised feature selection techniques in comparison to standard dimensionality reduction and supervised feature selection techniques on four commonly used benchmark datasets. The achieved classification results reveal that involving supervised feature selection techniques leads to similar classification results as involving unsupervised feature selection techniques, while the latter perform feature selection independently from the given classification task and thus deliver generally versatile features.

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Volumetric Directional Pattern for Spatial Feature Extraction in Hyperspectral Imagery

Cited by 13 publications

References 21 publications

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

High Order Volumetric Directional Pattern for Video‐Based Face Recognition

Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data

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