Special Issue "Algorithms in Hyperspectral Data Analysis"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: closed (15 January 2021).

Special Issue Editor

Dr. Raffaele Pizzolante
E-Mail Website
Guest Editor
Department of Computer Science, University of Salerno, I-84084 Fisciano (SA), Italy
Interests: data compression; information hiding; digital forensics; cyber security and digital watermarking

Special Issue Information

Dear Colleagues,

At present, thanks to the continuous evolution of sensor technologies for hyperspectral imaging, there is a high demand for the design of algorithms, techniques, and methods for the analysis of hyperspectral images. Hyperspectral images are a rich source of information, since they contain precious spatial and spectral contents, differently than traditional images.

In several research and real-life fields, hyperspectral images play an important role (e.g., agriculture, counter-terrorism, archaeology, forensic applications, environment monitoring, medicine). Furthermore, thanks to technology advances, it is now also possible to integrate the hyperspectral sensors in platforms where it was previously not possible (e.g., unmanned aerial vehicle platforms).

Therefore, in the future, there will be new scenarios and fields of application in which hyperspectral data will be involved in order to bring their precious contributions.

The aim of this Special Issue is therefore to welcome all relevant and recent advances from the scientific community, regarding research and studies related to the algorithms in hyperspectral data analysis.

The topics include but are not limited to the following areas:

  • Hyperspectral data classification;
  • Techniques and methods for data fusion;
  • Restoration;
  • Algorithms for hyperspectral unmixing;
  • Distributed and parallel processing;
  • Hyperspectral target detection;
  • Cloud platform for hyperspectral data analysis;
  • Supervised and semisupervised classification;
  • Lossless and lossy compression;
  • Real-time processing;
  • Analysis based on machine learning;
  • Data preprocessing

Dr. Raffaele Pizzolante
Guest Editor

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 papers will be 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. Algorithms is an international peer-reviewed open access monthly 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 1400 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 data
  • Hyperspectral imagery
  • Signal processing
  • Data analysis

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA
Algorithms 2021, 14(1), 18; https://0-doi-org.brum.beds.ac.uk/10.3390/a14010018 - 11 Jan 2021
Viewed by 467
Abstract
Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class [...] Read more.
Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system. Full article
(This article belongs to the Special Issue Algorithms in Hyperspectral Data Analysis)
Show Figures

Figure 1

Open AccessArticle
Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
Algorithms 2020, 13(12), 330; https://0-doi-org.brum.beds.ac.uk/10.3390/a13120330 - 10 Dec 2020
Viewed by 879
Abstract
Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed. The proposed four-step classification scheme explores how to effectively use the global spectral information and [...] Read more.
Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed. The proposed four-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Initially, multidimensional Watershed is used for pre-segmentation. Region-based hierarchical hyperspectral image segmentation is based on the construction of Binary partition trees (BPT). Each segmented region is modeled while using first-order parametric modelling, which is then followed by a region merging stage using HSI regional spectral properties in order to obtain a BPT representation. The tree is then pruned to obtain a more compact representation. In addition, principal component analysis (PCA) is utilized for HSI feature extraction, so that the extracted features are further incorporated into the BPT. Finally, an efficient variant of k-means clustering algorithm, called filtering algorithm, is deployed on the created BPT structure, producing the final cluster map. The proposed method is tested over eight publicly available hyperspectral scenes with ground truth data and it is further compared with other clustering frameworks. The extensive experimental analysis demonstrates the efficacy of the proposed method. Full article
(This article belongs to the Special Issue Algorithms in Hyperspectral Data Analysis)
Show Figures

Figure 1

Open AccessArticle
Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging
Algorithms 2020, 13(11), 289; https://0-doi-org.brum.beds.ac.uk/10.3390/a13110289 - 10 Nov 2020
Viewed by 625
Abstract
The preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this context. In this study, [...] Read more.
The preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this context. In this study, hyperspectral imaging (HSI) in the wavelength range of 500–995 nm was conducted for the determination of tissue water content (TWC) in kidneys. The quantitative relationship between spectral data and the reference TWC values was established by partial least squares regression (PLSR). Different preprocessing methods were applied to investigate their influence on predicting the TWC of kidneys. In the full wavelength range, the best models for absorbance and reflectance spectra provided Rp2 values of 0.968 and 0.963, as well as root-mean-square error of prediction (RMSEP) values of 2.016 and 2.155, respectively. Considering an optimal wavelength range (800–980 nm), the best model based on reflectance spectra (Rp2 value of 0.941, RMSEP value of 3.202). Finally, the visualization of TWC distribution in all pixels of kidneys’ HSI image was implemented. The results show the feasibility of HSI for a non-invasively and accurate TWC prediction in kidneys, which could be used in the future to assess the quality of kidneys during the preservation period. Full article
(This article belongs to the Special Issue Algorithms in Hyperspectral Data Analysis)
Show Figures

Figure 1

Open AccessArticle
A Fast Image Thresholding Algorithm for Infrared Images Based on Histogram Approximation and Circuit Theory
Algorithms 2020, 13(9), 207; https://0-doi-org.brum.beds.ac.uk/10.3390/a13090207 - 24 Aug 2020
Viewed by 855
Abstract
Image thresholding is one of the fastest and most effective methods of detecting objects in infrared images. This paper proposes an infrared image thresholding method based on the functional approximation of the histogram. The one-dimensional histogram of the image is approximated to the [...] Read more.
Image thresholding is one of the fastest and most effective methods of detecting objects in infrared images. This paper proposes an infrared image thresholding method based on the functional approximation of the histogram. The one-dimensional histogram of the image is approximated to the transient response of a first-order linear circuit. The threshold value for the image segmentation is formulated using combinational analogues of standard operators and principles from the concept of the transient behavior of the first-order linear circuit. The proposed method is tested on infrared images gathered from the standard databases and the experimental results are compared with the existing state-of-the-art infrared image thresholding methods. We realized through the experimental results that our method is well suited to perform infrared image thresholding. Full article
(This article belongs to the Special Issue Algorithms in Hyperspectral Data Analysis)
Show Figures

Graphical abstract

Back to TopTop