L1-2 Spectral-spatial Total Variation Regularized Hyperspectral Image Denoising

Hai-jin, 曾海金 ZENG; Jia-wei, 蒋家伟 JIANG; Jiajia, 赵佳佳 Zhao; Yi-zhuo, 王艺卓 WANG; Xiao-zhen, 谢晓振 XIE

doi:10.3788/gzxb20194810.1010002

Cited by 2 publications

(1 citation statement)

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“…( 55 ). This serves to mitigate the impact of background noise on sample spectral information while eliminating errors stemming from baseline drift, surface scattering, or noise introduced by uneven lighting or dark current in the lens ( 56 ), as hyperspectral images inevitably introduce noise information during the acquisition process, which will restrict the accuracy of image analysis ( 57 ). Data analysis involves the utilization of support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and other algorithms which combines both spectral and spatial features.…”

Section: Resultsmentioning

confidence: 99%

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

Jiang,

Ma,

Tan

et al. 2023

Front. Med.

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Hyperspectral imaging (HSI) is a promising technology that can provide valuable support for the advancement of the medical field. Bibliometrics can analyze a vast number of publications on both macroscopic and microscopic levels, providing scholars with essential foundations to shape future directions. The purpose of this study is to comprehensively review the existing literature on medical hyperspectral imaging (MHSI). Based on the Web of Science (WOS) database, this study systematically combs through literature using bibliometric methods and visualization software such as VOSviewer and CiteSpace to draw scientific conclusions. The analysis yielded 2,274 articles from 73 countries/regions, involving 7,401 authors, 2,037 institutions, 1,038 journals/conferences, and a total of 7,522 keywords. The field of MHSI is currently in a positive stage of development and has conducted extensive research worldwide. This research encompasses not only HSI technology but also its application to diverse medical research subjects, such as skin, cancer, tumors, etc., covering a wide range of hardware constructions and software algorithms. In addition to advancements in hardware, the future should focus on the development of algorithm standards for specific medical research targets and cultivate medical professionals of managing vast amounts of technical information.

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Section: Resultsmentioning

confidence: 99%

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

Jiang,

Ma,

Tan

et al. 2023

Front. Med.

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Spectral Characteristics Analysis and Extraction of Micro-Patches Based on the Hyperspectral Desert Steppe Images

Gao

et al. 2022

INMATEH

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In hyperspectral remote sensing images, desert steppe vegetation, bare soil, and rat holes appear as micro-patches. The spectral feature analysis of micro-patches is the basis for identification and classification and also the basis for quantitative remote sensing monitoring of ground objects. Inner Mongolia desert steppe micro-patch as the research object extracts the spectral reflectance of different micro-patches, performs various vegetation index calculations, quantitatively analyzes the spectral characteristics of different micro-patches, and proposes a micro-patch spectral analysis method. Classification of high-resolution hyperspectral images of desert steppe surface micropatches. The results show that: (1) There are pronounced differences in the spectral reflectance of the three types of surface micro-patches. The vegetation has apparent characteristics in the green wave reflection peak and the red wave absorption valley. The spectral reflectance of the bare soil is higher than that of the mouse hole, and the two have been increasing. The trend is increasing slowly; (2) The proposal and application of the MSA index can effectively realize the identification and classification of surface micropatches, and the Kappa coefficient has reached 0.906 through confusion matrix verification. The above spectral analysis method realizes the classification and identification of complex ground objects using near-ground remote sensing images. It provides new ideas and methods for accurate quantitative statistics of desert grassland ecological information.

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L1-2 Spectral-spatial Total Variation Regularized Hyperspectral Image Denoising

Cited by 2 publications

References 0 publications

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

Spectral Characteristics Analysis and Extraction of Micro-Patches Based on the Hyperspectral Desert Steppe Images

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