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Research and Application of Robust Hyperspectral Image

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (25 December 2021) | Viewed by 3811

Special Issue Editors

Special Issue Information

Dear Colleagues,

Multispectral/hyperspectral images contain both the spatial and spectral information of a scene. It can also take advantage of the spatial relationships between different spectra in a neighborhood, allowing more elaborate spectral–spatial models for more accurate segmentation and classification of the image. At present, hyperspectral image technology has found important applications in many fields such as military reconnaissance, smart agriculture, geological survey, food safety, industrial sorting, and bio-medicine.

Due to the advantage of the spectral–spatial data, there are currently both opportunities and challenges in the development of optical imaging devices, data acquisition, quality improvement, information processing, and intelligent analysis. Although hyperspectral images contain rich spatial and spectral information, and have great potential for material attribute identification, how to effectively use the data characteristics of hyperspectral images to maximize their advantages in diverse application scenarios has become a focus for the industry.

To promote the in-depth development of related technologies and applications in the hyperspectral image field, this Special Issue will provide a forum for new research on robust hyperspectral image processing technologies. Considering the scarcity of hyperspectral data set resources, the large redundancy of hyperspectral image information, and the absence of robust data processing methods, it is likely that hyperspectral image processing technology will gain more popularity. Thus, related techniques are also welcome in this Special Issue.

Prof. Dr. Jiayi Ma
Prof. Dr. Xiaoguang Mei
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Hyperspectral image classification
  • Endmember extraction and spectral unmixing
  • Hyperspectral anomaly detection
  • Hyperspectral image super-resolution
  • Band selection
  • Dimensionality reduction
  • Hyperspectral image denoising
  • Parallel hyperspectral image and signal processing

Published Papers (1 paper)

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Research

19 pages, 2920 KiB  
Article
Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model
by Weihua Liu, Shan Zeng, Guiju Wu, Hao Li and Feifei Chen
Sensors 2021, 21(13), 4384; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134384 - 26 Jun 2021
Cited by 21 | Viewed by 2970
Abstract
Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of [...] Read more.
Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67–100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60–100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification. Full article
(This article belongs to the Special Issue Research and Application of Robust Hyperspectral Image)
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