Mineral identification in LWIR hyperspectral imagery applying sparse-based clustering

Yousefi, Bardia; Ibarra-Castanedo, Clemente; Bédard, Émilie; Beaudoin, Georges; Maldague, Xavier

doi:10.1080/17686733.2018.1550902

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…In addition, there are many self-built data sets, such as the microscopic image dataset of heavy minerals, the infrared dataset of nine different minerals, and the image sample database of rocks and minerals. [75,76] Table 1. Mineral public database.…”

Section: Automatic Data Collectionmentioning

confidence: 99%

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Zhang,

Yang,

Sun

et al. 2023

Advanced Intelligent Systems

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Artificial intelligence promotes the development of mineral/rock automatic recognition technology to reduce labor costs and reliance on personal experience. With instrument digitization, image recognition has played an increasingly important role. This review focuses on the image sensing system applied to mineral/rock identification and redivides this system into a sensing module, an imaging module, and an information processing module. The sensing and imaging mechanisms are summarized via technologies of sensing, imaging, and display. The information processing is classified into single‐modal or multimodal recognition. This review analyzes the limitations of single‐modal recognition and discusses the advantages and significance of multimodal recognition. The challenges of current mineral/rock identification are concluded, such as public database, multimodal learning, and portable sensing system. The interdisciplinary sensing technologies are discussed, and application feasibility is explored. This review aims to provide a reference for the research community in automatic mineral/rock identification and sensing system optimization.

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Section: Automatic Data Collectionmentioning

confidence: 99%

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Zhang,

Yang,

Sun

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…The ease of implementation and fast operation on large datasets are the main advantages of K-means clustering. Menesatti Paolo [34], Shoa Pedram [35], and Yousefi Bardia [36] et al successfully applied the clustering analysis method to cluster infrared thermal images. These studies provide important references for the evaluation of the homogeneity of silane coatings.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Uniformity of Protective Coatings on Concrete Structure Surfaces Based on Cluster Analysis

Liu

Zhang

Tang

et al. 2021

Sensors

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With the continuous development of urbanization and industrialization in the world, concrete is widely used in various engineering constructions as an engineering material. However, the consequent problem of durability of concrete structures is also becoming increasingly prominent. As an important additional measure, a protective coating can effectively improve the durability of concrete performance. Moreover, the uniformity of the concrete surface coating will directly affect its protective effect. Therefore, we propose a nondestructive inspection and evaluation method of coating uniformity based on infrared imaging and cluster analysis for concrete surface coating uniformity detection and evaluation. Based on the obtained infrared images, a series of processing and analysis of the images were carried out using MATLAB software to obtain the characteristics of the infrared images of the concrete surface. Finally, by extracting the temperature distribution data of the pixel points on the concrete surface, an evaluation method of concrete surface coating uniformity based on a combination of cluster analysis and hierarchical analysis was established. The evaluation results show that the determination results obtained by this method are consistent with the actual situation. This study has a positive contribution to the testing of concrete surface coating uniformity and its evaluation.

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Hyperspectral imagery classification based on semi-supervised 3-D deep neural network and adaptive band selection

Sellami

Farah

et al. 2019

Expert Systems with Applications

114

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Mineral identification in LWIR hyperspectral imagery applying sparse-based clustering

Cited by 6 publications

References 39 publications

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Evaluation of the Uniformity of Protective Coatings on Concrete Structure Surfaces Based on Cluster Analysis

Hyperspectral imagery classification based on semi-supervised 3-D deep neural network and adaptive band selection

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