Development and processing of hyperspectral images in optical–electronic remote sensing systems

Kozinov, I. A.; Maltsev, G. N.

doi:10.1134/s0030400x16120158

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…After achieving the purpose of simplifying the model, it then improves the predictive ability of the model [ 48 ]. MA is used to take the average of the data in a certain time period and use this average to represent the data in that time period, thus achieving the purpose of smoothing the data [ 49 ]. Spectral data contain information about the sample, but there may be some unrelated underlying trends in the data.…”

Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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“…In order to reduce the computational cost of spectra analysis, the selection of the most informative spectra channels can be used. A proposal for such a method is presented in [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Imaging for Mobile Robot Navigation

Jakubczyk

Siemiątkowska

Więckowski

et al. 2022

Sensors

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The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of colors and features than traditional cameras and to perceive the environment more accurately. Several surface types, such as mud, can be challenging to detect using an RGB camera. In our system, the hyperspectral camera is used for ground recognition (e.g., grass, bumpy road, asphalt). Traditional global path planning methods take the shortest path length as the optimization objective. We propose an improved A* algorithm to generate the collision-free path. Semantic information makes it possible to plan a feasible and safe path in a complex off-road environment, taking traveling time as the optimization objective. We presented the results of the experiments for data collected in a natural environment. An important novelty of this paper is using a modified nearest neighbor method for hyperspectral data analysis and then using the data for path planning tasks in the same work. Using the nearest neighbor method allows us to adjust the robotic system much faster than using neural networks. As our system is continuously evolving, we intend to examine the performance of the vehicle on various road surfaces, which is why we sought to create a classification system that does not require a prolonged learning process. In our paper, we aimed to demonstrate that the incorporation of a hyperspectral camera can not only enhance route planning but also aid in the determination of parameters such as speed and acceleration.

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“…6−8 have shown that the use of several spectral images within certain ranges allows to more clearly distinguish the regions of the gradient values and structural functions corresponding to them. According to [20], selection of the most informative spectral components allows to decrease the losses in confidence during recognition by limited number of spectral channels. This is why the selection of the most informative spectral components can be made at the imaging planning stage based on the information a priori about the observed objects and background or during the imaging with the controlled fragmental registration by spectrum, for example, based on acoustic and optic filters [2,21].…”

Section: Examples Of Images Processingmentioning

confidence: 99%

Method of selection of objects on a hyperspectral image based on the analysys of their contours

Shipko

Волобуев

2022

Optics and Spectroscopy

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A new method of spectral selection of given objects on hyperspectral images is considered. At the first stage of the method, hypotheses are tested using the Neyman-Pearson criterion about the presence of object contours in neighboring pixels relative to the simple alternative of their absence consistently over all spectral components. If a decision is made about the presence of a contour in at least one spectral channel, these pixels are analyzed at the second stage with respect to their distribution over the spectral range according to the criterion of maximum a posteriori probability density. Given the values of the mathematical expectation of the gradient difference between the spectral components, hypotheses are formed about the presence or absence of the contour of the desired object. The decision is made on the basis of a comparison of the decision statistics with the likelihood functions. The characteristics of detection and the results of experiments performed on real images are presented. Keywords: hyperspectral images, contour, gradient, likelihood function.

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Development and processing of hyperspectral images in optical–electronic remote sensing systems

Cited by 5 publications

References 3 publications

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Hyperspectral Imaging for Mobile Robot Navigation

Method of selection of objects on a hyperspectral image based on the analysys of their contours

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