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Applied Sciences
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Advances in Deep Learning for Hyperspectral Image Processing
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Advances in Deep Learning for Hyperspectral Image Processing

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2168

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,

These days, artificial intelligence (AI) is applied in almost every domain of life, including in satellite remote sensing, medical image processing and so on. With the development of imaging technology, images with very high spectral resolution are proving to be an important asset in areas such as land use, land coverage, meteorology, vegetation mapping, military applications, disaster risk management, change detection, assisting diagnosis, predicting patient outcome, etc. Recent developments in imaging technology and AI technologies have provided great opportunities to develop reliable, accurate and time-effective solutions and indicators to overcome climate and environmental change challenges. Regarding the massive amount of hyperspectral image (HSIs) data available, it is difficult to meet the growing demand for hyperspectral image applications by interpreting the images manually. Therefore, the need for methods to interpret HSIs automatically, efficiently and accurately presents a significant challenge in the research and application of HSIs technology. Recently, artificial intelligence technologies, such as machine learning and deep learning, have been shown to have potential to overcome the challenges of hyperspectral image recognition, classification, segmentation and spectral analysis. 

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • deep learning
  • artificial intelligence
  • machine learning
  • hyperspectral image processing
  • image classification
  • object detection
  • change detection
  • image segmentation

Published Papers (2 papers)

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Research

18 pages, 4243 KiB  
Article
PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
by Xiaoqin Xue, Chao Ren, Anchao Yin, Ying Zhou, Yuanyuan Liu, Cong Ding and Jiakai Lu
Appl. Sci. 2024, 14(4), 1634; https://doi.org/10.3390/app14041634 - 18 Feb 2024
Viewed by 409
Abstract
In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convolution, and Dual-Inut Cross Attention (DCA) modules for optimized [...] Read more.
In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convolution, and Dual-Inut Cross Attention (DCA) modules for optimized performance. Initially, the Pyramid Pathway Input equips the model to identify features at multiple scales, markedly enhancing its ability to discriminate between roads and other background elements. Secondly, by adopting CoordConv convolutional layers, the model achieves heightened accuracy in road recognition and extraction against complex backdrops. Moreover, the DCA module serves dual purposes: it is employed at the encoder stage to efficiently consolidate feature maps across scales, thereby fortifying the model’s road detection capabilities while mitigating false positives. In the skip connection stages, the DCA module further refines the continuity and accuracy of the features. Extensive empirical evaluation substantiates that PCCAU-Net significantly outperforms existing state-of-the-art techniques on multiple benchmarks, including precision, recall, and Intersection-over-Union(IoU). Consequently, PCCAU-Net not only represents a considerable advancement in road extraction research, but also demonstrates vast potential for broader applications, such as urban planning and traffic analytics. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Hyperspectral Image Processing)
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20 pages, 14922 KiB  
Article
Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds
by Shufang Xu, Sijie Geng, Qi Yang and Hongmin Gao
Appl. Sci. 2023, 13(16), 9180; https://doi.org/10.3390/app13169180 - 11 Aug 2023
Viewed by 848
Abstract
Hyperspectral images contain rich spatial–spectral information and have high dimensions, which can lead to challenges related to feature extraction for classification tasks, resulting in suboptimal performance. We propose a hyperspectral image dimensionality reduction algorithm based on spatial–spectral adaptive multiple manifolds to address the [...] Read more.
Hyperspectral images contain rich spatial–spectral information and have high dimensions, which can lead to challenges related to feature extraction for classification tasks, resulting in suboptimal performance. We propose a hyperspectral image dimensionality reduction algorithm based on spatial–spectral adaptive multiple manifolds to address the problem of small differences between features of dissimilar samples in the subspace caused by the uniform projection transformation in traditional dimensionality reduction methods. Firstly, to address spatial boundary mismatch problems caused by re-characterizing a pixel using pixels in a fixed area around it as its near neighbors in traditional algorithms, an adaptive weight representation method based on super-pixel segmentation is proposed, which enhances the similarity of similar samples and the dissimilarity of dissimilar samples. Secondly, to address the problem that a single manifold cannot completely characterize the near neighbor between samples of different categories, an adaptive multi-manifold representation method is proposed. The feature representation of the entire hyperspectral data in the low-dimensional subspace is obtained by adaptively fusing the intra- and inter-manifold maps constructed for each category of samples in the spatial and spectral dimensions. Experimental results on two public datasets show that the proposed method achieves better results when performing the hyperspectral image dimensionality reduction task. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Hyperspectral Image Processing)
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