A Special Issue on Hyperspectral Imaging [From the Guest Editors]

Parente, M.; Kerekes, John P.; Heylen, Rob

doi:10.1109/mgrs.2019.2912617

Cited by 8 publications

(7 citation statements)

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“…These spectral signatures enable identifying the materials, a scanned object or target is made of. With a hyper spectral camera, it is possible to know and get the chemical composition of the objects at distance with a hyper spectral camera (17,18).…”

Section: Multispectral and Hyper Spectral Imagingmentioning

confidence: 99%

IoT, Enabling Technologies, and Sensor Node Deployment Pattern in WSN

Gupta¹,

Nawal²,

Janu³

et al. 2022

ECS Trans.

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In Internet of Things (IoT), various computing devices and mechanical and digital machines are interconnected. These devices have unique identifiers (UIDs) and transmit the information on the network with no human interaction. Sensors are the integrated part in IoT as these are used to collect the data. The IoT technologies have a lot of issues like addressing problems, scalability problems, security, and standardization issues that required to be solved. In this paper, the authors facilitate the reader to have necessary understanding of IoT, importance on protocols, technologies, application related issues, various types of sensors used in IoT, new generation of alternative sensors for IoT, and the issues which needs to resolved for the future. A depth overview of 5G IoT systems is also given. The five layers in 5G IoT systems and empowering technologies associated with it are discussed. A comparative analysis of QC-PC-MCSC for strip based deployment pattern and for random deployment is given. This paper provides a support to academician about the working of diverse protocols, relation between IoT and other emergent technologies together with big data and cloud, energy efficiency based on sensor node deployment pattern, etc.

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Section: Multispectral and Hyper Spectral Imagingmentioning

confidence: 99%

IoT, Enabling Technologies, and Sensor Node Deployment Pattern in WSN

Gupta¹,

Nawal²,

Janu³

et al. 2022

ECS Trans.

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“…Unlike traditional color or grayscale images, hyperspectral images (HSI) provide not only two-dimensional spatial data but also abundant spectral information from numerous bands (usually ranging from tens to hundreds), enabling the visualization of material characteristics at different wavelengths. Hyperspectral images allow for more accurate identification and classification of different substances, finding wide applications in fields such as remote sensing imaging [1][2][3][4] , industrial inspection 5 , medical imaging 6 , and disaster monitoring [7][8][9] . However, hyperspectral image data is large in size, with each pixel containing spectral information from multiple wavelengths, resulting in substantial resource consumption during transmission and storage, especially in areas such as real-time display during airborne transmission and high-capacity wireless transmission of hyperspectral images [10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

Research on optimization of CCSDS compression algorithm based on snapshot mosaic hyperspectral images

Ping,

Feng,

Tian

et al. 2024

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024)

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“…Spectral images with a spectral resolution in the 10-2 order of magnitude are called hyperspectral images (HSI). HSI is one of the main components of modern remote sensing [1]. In recent decades, the popularity of hyperspectral technology has continued to rise.…”

Section: Introductionmentioning

confidence: 99%

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

et al. 2021

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The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing. Compressed sensing technology is an important tool for retrieving the maximum number of HSI scenes on the ground. However, the complexity of the compressed sensing algorithm is limited by the energy and hardware of spaceborne equipment. Aiming at the high complexity of compressed sensing reconstruction algorithm and low reconstruction accuracy, an equivalent model of the invertible transformation is theoretically derived by us in the paper, which can convert the complex invertible projection training model into the coupled dictionary training model. Besides, aiming at the invertible projection training model, the most competitive task-driven invertible projection matrix learning algorithm (TIPML) is proposed. In TIPML, we don’t need to directly train the complex invertible projection model, but indirectly train the invertible projection model through the training of the coupled dictionary. In order to improve the accuracy of reconstructed data, in the paper, the singular value transformation is proposed. It has been verified that the concentration of the dictionary is increased and that the expressive ability of the dictionary has not been reduced by the transformation. Besides, two-loop iterative training is established to improve the accuracy of data reconstruction. Experiments show that, compared with the traditional compressed sensing algorithm, the compressed sensing algorithm based on TIPML has higher reconstruction accuracy, and the reconstruction time is shortened by more than a hundred times. It is foreseeable that the TIPML algorithm will have a huge application prospect in the field of HSI compression.

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A Special Issue on Hyperspectral Imaging [From the Guest Editors]

Cited by 8 publications

References 0 publications

IoT, Enabling Technologies, and Sensor Node Deployment Pattern in WSN

IoT, Enabling Technologies, and Sensor Node Deployment Pattern in WSN

Research on optimization of CCSDS compression algorithm based on snapshot mosaic hyperspectral images

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

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