Spectral Data Classification By One-Dimensional Convolutional Neural Networks

Zeng, Fanguo; Wen, Peng; Kang, Gaobi; Feng, Zekai; Yue, Xuejun

doi:10.1109/ipccc51483.2021.9679444

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…This approach improves peak filtering performance by reducing the false peaks by more than 90% compared to the traditional chemometric methods. Zeng et al 13 utilized one-dimensional convolutional neural network (CNN) to classify the visible-near infrared spectra of corn seed to evaluate seed viability. In addition, Lee et al 14 developed a CNN-based model to classify interested phases from a mixture of inorganic compounds using XRD.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced Spectral Data Analysis using Data Augmentation based on the Generative Adversarial Network

Chung,

Zhang,

Saimon

et al. 2024

Preprint

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Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 7%, 5%, and 5% of the performance of the existing data augmentation methods regarding the classifier’s F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method’s broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering.

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Section: Introductionmentioning

confidence: 99%

Imbalanced Spectral Data Analysis using Data Augmentation based on the Generative Adversarial Network

Chung,

Zhang,

Saimon

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…On the other hand, Artificial Neural Networks (ANNs) have emerged as a powerful tool for the predictive modeling of big data. Moreover, machine learning approaches such as Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) have been shown to be superior to PLS-DA in classifying omics, phenotypic, and spectral data [ 11 , 19 ]. The big drawback is that many of these methods (CNN and MLP), in contrast to the measure-based methods (such as FlowerMorphology morphometry software [ 20 ]), are “black boxes”, so they do not disclose all feature interactions and do not provide classification rules, and the results referred to can hardly be understood by the user [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments

Ballesta

Maldonado

Mora‐Poblete

et al. 2023

Plants

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The global concern about the gap between food production and consumption has intensified the research on the genetics, ecophysiology, and breeding of cereal crops. In this sense, several genetic studies have been conducted to assess the effectiveness and sustainability of collections of germplasm accessions of major crops. In this study, a spectral-based classification approach for the assignment of wheat cultivars to genetically differentiated subpopulations (genetic structure) was carried out using a panel of 316 spring bread cultivars grown in two environments with different water regimes (rainfed and fully irrigated). For that, different machine-learning models were trained with foliar spectral and genetic information to assign the wheat cultivars to subpopulations. The results revealed that, in general, the hyperparameters ReLU (as the activation function), adam (as the optimizer), and a size batch of 10 give neural network models better accuracy. Genetically differentiated groups showed smaller differences in mean wavelengths under rainfed than under full irrigation, which coincided with a reduction in clustering accuracy in neural network models. The comparison of models indicated that the Convolutional Neural Network (CNN) was significantly more accurate in classifying individuals into their respective subpopulations, with 92 and 93% of correct individual assignments in water-limited and fully irrigated environments, respectively, whereas 92% (full irrigation) and 78% (rainfed) of cultivars were correctly assigned to their respective classes by the multilayer perceptron method and partial least squares discriminant analysis, respectively. Notably, CNN did not show significant differences between both environments, which indicates stability in the prediction independent of the different water regimes. It is concluded that foliar spectral variation can be used to accurately infer the belonging of a cultivar to its respective genetically differentiated group, even considering radically different environments, which is highly desirable in the context of crop genetic resources management.

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“…Despite their prevalent application in multidimensional image data analysis [24,25], CNNs prove impractical for analyzing one-dimensional (1D) structured data, such as leaf-level reflectance data (LLRD), due to implicit constraints of lower dimensionality and limited sample size [26]. A plausible solution could be to transform 1D LLRD into a two-dimensional (2D) matrix [27,28].…”

Section: Introductionmentioning

confidence: 99%

Reshaping Leaf-Level Reflectance Data for Plant Species Discrimination: Exploring Image Shape’s Impact on Deep Learning Results

Yuan,

Song,

Gong

et al. 2023

Remote Sensing

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The application of hyperspectral imagery coupled with deep learning shows vast promise in plant species discrimination. Reshaping one-dimensional (1D) leaf-level reflectance data (LLRD) into two-dimensional (2D) grayscale images as convolutional neural network (CNN) model input demonstrated marked effectiveness in plant species distinction. However, the impact of the image shape on CNN model performance remained unexplored. This study addressed this by reshaping data into fifteen distinct rectangular formats and creating nine CNN models to examine the effect of image structure. Results demonstrated that irrespective of CNN model structure, elongated narrow images yielded superior species identification results. The ‘l’-shaped images at 225 × 9 pixels outperformed other configurations based on 93.95% accuracy, 94.55% precision, and 0.94 F1 score. Furthermore, ‘l’-shaped hyperspectral images consistently produced high classification precision across species. The results suggest this image shape boosts robust predictive performance, paving the way for enhancing leaf trait estimation and proposing a practical solution for pixel-level categorization within hyperspectral imagery (HSIs).

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Spectral Data Classification By One-Dimensional Convolutional Neural Networks

Cited by 6 publications

References 18 publications

Imbalanced Spectral Data Analysis using Data Augmentation based on the Generative Adversarial Network

Imbalanced Spectral Data Analysis using Data Augmentation based on the Generative Adversarial Network

Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments

Reshaping Leaf-Level Reflectance Data for Plant Species Discrimination: Exploring Image Shape’s Impact on Deep Learning Results

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