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Applied Sciences
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Hyperspectral Image: Research and Applications
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Hyperspectral Image: Research and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 794

Special Issue Editors

Prof. Dr. Hongmin Gao

E-Mail Website
Guest Editor
College of Information Science and Engineering, Hohai University, Nanjing 210098, China
Interests: deep learning; image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Mingxiang Yang

E-Mail Website
Guest Editor
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Interests: deep learning; information fusion; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advances in hyperspectral imaging have significantly enhanced our capacity to collect intricate data pertaining to the Earth's surface. Nonetheless, effectively harnessing this wealth of spectral information remains a formidable task. Deep learning has emerged as a promising avenue, fundamentally transforming the classification of hyperspectral images by autonomously discerning complex spectral–spatial patterns. We invite contributions that push the boundaries of current knowledge in this dynamic domain, aiming to unlock fresh perspectives for more precise and impactful applications.

This Special Issue seeks to delve into the latest breakthroughs in leveraging deep learning methodologies for hyperspectral image research and applications. Researchers are urged to submit original research articles, reviews, or surveys. Submissions must uphold rigorous scientific standards, underscore the significance of their findings, and provide transparent experimental validation. We embrace submissions that tackle both theoretical advancements and practical applications.

Prof. Dr. Hongmin Gao
Dr. Mingxiang Yang
Guest Editors

Manuscript Submission Information

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Keywords

  • hyperspectral image
  • deep learning
  • artificial intelligence
  • image processing
  • image classification

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Published Papers (1 paper)

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Research

15 pages, 2400 KiB  
Article
SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images
by Behnam Asghari Beirami, Mehran Alizadeh Pirbasti and Vahid Akbari
Appl. Sci. 2024, 14(16), 7361; https://doi.org/10.3390/app14167361 - 21 Aug 2024
Viewed by 577
Abstract
One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification. Despite this, researchers continue to study and propose simpler, more robust models. This study presents [...] Read more.
One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification. Despite this, researchers continue to study and propose simpler, more robust models. This study presents a novel deep-learning approach, the iterative convolutional neural network (ICNN), which combines spectral–fractal features and classifier probability maps iteratively, aiming to enhance the HSI classification accuracy. Experiments are conducted to prove the accuracy enhancement of the proposed method using HSI benchmark datasets of Indian pine (IP) and the University of Pavia (PU) to evaluate the performance of the proposed technique. The final results show that the proposed approach reaches overall accuracies of 99.16% and 95.5% on the IP and PU datasets, respectively, which are better than some basic methods. Additionally, the end findings demonstrate that greater accuracy levels might be achieved using a primary CNN network that employs the iteration loop than with certain current state-of-the-art spatial–spectral HSI classification techniques. Full article
(This article belongs to the Special Issue Hyperspectral Image: Research and Applications)
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