Identification of Typical Solid Hazardous Chemicals Based on Hyperspectral Imaging

Sun, Yanlong; Qian, Xinming; Liu, Yangyang; Wang, Jianwei; Lv, Qunbo; Yuan, Mengqi

doi:10.3390/rs13132608

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…This method provides a noncontact and effective means to monitor hazardous chemical spills in rivers, mitigating the constraints associated with traditional contact-based measurement approaches. In [142], the study explores the application of HSI for the detection and recognition of hazardous chemicals. They employ a combination of spectral curve matching based on full-waveform characteristics and spectral matching based on spectral properties.…”

Section: Safety and Securitymentioning

confidence: 99%

Hyperspectral Imaging Algorithms and Applications: A Review

Akewar,

Chandak

2023

Preprint

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<p>The paper covers topics ranging from hyperspectral imaging applications to innovative algorithms that have enhanced the analysis of hyperspectral data. We delve into the practical applications, including Agriculture, Food Quality and Safety, Earth Sciences, Exploration and Monitoring, Healthcare, Pharmaceuticals, Medical Imaging, Industrial Manufacturing, Management, Conservation, Safety and Security. Our goal is to provide a holistic understanding of how hyperspectral imaging is transforming these fields and driving new possibilities. We also discussed detailed algorithmic development in Hyperspectral Imaging including Supervised and Unsupervised algorithms. We reviewed algorithmic development from the early 1980’s to recent developments including Artificial Intelligence involving algorithms from Machine Learning to Deep Learning. We believe our paper will be of particular interest to your readers for those who wish to study algorithm development from simple image processing procedures to AI based approaches and how these algorithms are used in diverse applications to make intelligent decisions for increasing productivity and efficiency in Agriculture, Exploration and Management and identification of diseases and natural resources. Our paper is well organized into various sections as per algorithms and applications. We reviewed more than 250 papers with proper citations and authors' research work.</p>

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Section: Safety and Securitymentioning

confidence: 99%

Hyperspectral Imaging Algorithms and Applications: A Review

Akewar,

Chandak

2023

Preprint

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Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification

Bai

Sun

et al. 2023

Multimed Tools Appl

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Deep learning models have been widely used in hyperspectral images classification. However, the classification results are not satisfactory when the number of training samples is small. Focused on above-mentioned problem, a novel Two-stage Multi-dimensional Convolutional Stacked Autoencoder (TMC-SAE) model is proposed for hyperspectral images classification. The proposed model is composed of two sub-models SAE-1 and SAE-2. The SAE-1 is a 1D autoencoder with asymmetric structre based on full connection layers and 1D convolution layers to reduce spectral dimensionality. The SAE-2 is a hybrid autoencoder composed of 2D and 3D convolution operations to extract spectral-spatial features from the reduced dimensionality data by SAE-1. The SAE-1 is trained with raw data by unsupervised learning and the encoder of SAE-1 is employed to reduce spectral dimensionality of raw data. The data after dimension reduction is used to train the SAE-2 by unsupervised learning. The fine-tuning of SAE-2 encoder and the training of classifier are implemented simultaneously with small number of samples by supervised learning. Comparative experiments are performed on three widely used hyperspectral remote sensing data. The extensive comparative experiments demonstrate that the proposed architecture can effectively extract deep features and maintain high classification accuracy with small number of training samples.

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