Classification of oat and groat kernels using NIR hyperspectral imaging

Serranti, Silvia; Cesare, Daniela; Marini, Federico; Bonifazi, Giuseppe

doi:10.1016/j.talanta.2012.10.044

Cited by 104 publications

(61 citation statements)

References 25 publications

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“…The second trend we consider, is the increasing attention on small-scale applications of hyperspectral imaging (HSI) in several domains, such as industrial quality controls (especially food, pharma and chemical [3,4]), cultural heritage preservation [5] and a number of biomedical applications [6]. What currently contributes to the proliferation and diversification of small-scale HSI applications, in addition to the classical Remote Sensing (RS) ones, is the increasing technological variety and ever lower cost of acquisition equipment [7,8] and, as for DL, the continued increase in computational power and storage/transmission capabilities of computing hardware and networks.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology

Turra

Arrigoni²,

Signoroni

2017

Image Analysis and Processing - ICIAP 2017

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Abstract. The rapidly increasing diffusion of Full Microbiology Laboratory Automation plants is reshaping the way microbiologists perform diagnostic tasks. A huge stream of digital visual data is expected to be produced daily in the coming years in the emerging field of Digital Microbiology Imaging. In this context, we want to assess the suitability and effectiveness of a Deep Learning approach to solve the diagnostically relevant but visually challenging task of directly identifying pathogens on bacterial growing plates. In particular, starting from hyperspectral acquisitions in the VNIR range and spatial-spectral processing of cultured plates, we approach the identification problem as the classification of computed spectral signatures of the bacterial colonies. In a highly relevant clinical context (urinary tract infections) and on a database of HSI images, we designed and trained a Convolutional Neural Network for pathogen identification, assessing its performance and comparing it against conventional classification solutions.

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Section: Introductionmentioning

confidence: 99%

CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology

Turra

Arrigoni²,

Signoroni

2017

Image Analysis and Processing - ICIAP 2017

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“…There are several analysis techniques to distinguish a target region using hyperspectral imaging data. These techniques include the Gabor filter, grey level co-occurrence matrix (GLCM), principle component analysis (PCA), minimum noise fraction (MNF), and wavelet transform [14][15][16][17]. In many cases, these methods assume the use of all wavelength bands of the hyperspectral image.…”

Section: Introductionmentioning

confidence: 99%

In vivo tumor wavelength band selection using Hierarchical clustering and PCA with NIR-Hyperspectral Data

Kasai

Yasuda

Mizoguchi

et al. 2017

JBEMi

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This paper presents a new method of wavelength selection combined with principle component analysis (PCA) and a hierarchal method for hyperspectral data analysis. Hyperspectral data analysis is a combination of imaging and spectroscopic technology, and is utilized in several fields. In the medical field, if it is possible to distinguish the region of interest by selecting a feature wavelength without the intervention of manufacturers, spectral application as a surgery support system would be feasible. There are several analysis techniques to extract the features using hyperspectral data; however, many of these methods are premised on using all wavelengths. Considering application to endoscopes and other medical devices and reducing number of wavelengths, these methods are not applicable. PCA is a popular analysis for reducing the number of wavelengths, and has been adapted for extracting general and clear features of specimens. However, such extracted features from the body tend to highlight the features of protein and blood making it difficult to extract the features of cancer or other diseases. This paper proposes a two-type feature extraction method that depends on "difference" and "similarity". In this method, the PCA and hierarchal classification analysis are combined to merge the clusters based on similarity. The feasibility of the proposed method is verified by applying the method to discriminating the cancer of mouse: colonized cancer tissue after three weeks and colonized cancer tissue after one week. The results show that the proposed method can extract the two-type feature. The proposed method is had an accuracy of 75.9% in reducing the number of wavelength bands from 256 to 8 for mature cancer in mice. Although less accurate than using all wavelength bands, the eight wavelength bands are distinguished using a simplified convolutional neural network model with an accuracy of 75.9%. The proposed method is able to select the feature wavelength bands for the target. As the learning model for improving the accuracy advances beyond the simple model and the new feature is extracted in the selected wavelength bands, the method is applied to a diagnosis supporting system for endoscopic surgery and more.

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“…Hyperspectral imaging technology can provide the spectral and image features of measured objects, but the spectral features are the most commonly used in seed variety classification because of their easy calculation [11][12][13][14]. The model based on spectral features will have the risk of classification accuracy deterioration if there are many seed varieties to be classified, or only a small amount of spectral information of seeds can be used because the need for rapid detection should be met.…”

Section: Introductionmentioning

confidence: 99%

Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging

et al. 2016

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Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety. A novel procedure for the classification of maize seed varieties based on HSI was proposed in this study. The optimal wavelengths for the classification of maize seed varieties were selected using the successive projections algorithm (SPA) to improve the acquiring and processing speed of HSI. Subsequently, spectral and imaging features were extracted from regions of interest of the hyperspectral images. Principle component analysis and multidimensional scaling were then introduced to transform/reduce the classification features for overcoming the risk of dimension disaster caused by the use of a large number of features. Finally, the integrating features were used to develop a least squares-support vector machines (LS-SVM) model. The LS-SVM model, using the integration of spectral and image features combined with feature transformation methods, achieved more than 90% of test accuracy, which was better than the 83.68% obtained by model using the original spectral and image features, and much higher than the 76.18% obtained by the model only using the spectral features. This procedure provides a possible way to apply the multispectral imaging system to classify seed varieties with high accuracy.

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Classification of oat and groat kernels using NIR hyperspectral imaging

Cited by 104 publications

References 25 publications

CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology

CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology

In vivo tumor wavelength band selection using Hierarchical clustering and PCA with NIR-Hyperspectral Data

Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging

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