Journal Browser

► Journal Browser

Special Issue Editors

E-Mail Website
Guest Editor

Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia
Interests: remote sensing domain; multispectral sensors; data fusion; image fusion; multicriteria analysis

Dr. Anna Brook

E-Mail Website
Guest Editor

Spectroscopy and Remote Sensing Laboratory, Department of Geography and Environmental Studie, Faculty of Social Science, University of Haifa, Haifa 3498838, Israel
Interests: data fusion; image and signal processing; automation target recognition; sub-pixel detection; spectral models across NIR-MIR regions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Paul Geladi

E-Mail Website
Guest Editor

Biomass Technology and Chemistry, Swedish University of Agricultural Sciences, Skogsmarksgränd, 90183 Umeå, Sweden
Interests: multivariate data analysis; classification, calibration; near-infrared spectroscopy; hyperspectral imaging

Special Issue Information

Dear Colleagues,

Hyperspectral imaging has become a huge topic and one of most promising techniques in remote sensing with a number of applications in very varied areas of research. Macroscopic hyperspectral imaging was mostly implemented by satellites and airplanes, but with today’s development of cheaper drones, hyperspectral measurement increases the possibilities of use to unimaginable limits (agronomy, forestry, geology, archeology, hydrology, ecology). On the other hand, with the rapid development of hyperspectral sensors and illumination sources, laboratory and field hyperspectral imaging provide many new applications (industry, engineering, archeology, medicine). The rapid growth of this field has made it difficult for many researchers to keep up with the development and advancement of its technology, including data handling, analysis, and presentation. Thus, this Special Issue aims to address the current status of technology and application in any of the above mentioned areas of use of hyperspectral measurements and remote sensing methods. Papers exploring the innovative possibilities of using hyperspectral sensors and ways of processing and obtaining results from them for different areas of human activity are invited.

Dr. Andrija Krtalić
Dr. Anna Brook
Prof. Dr. Paul Geladi
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 2300 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

Spectral signature
Spectral interpretation
Spatial scanning
Multivariate classification and regression
Spectral scanning
Spectral angle mapping

Published Papers (2 papers)

Download All Papers

Research

Jump to: Review

Open AccessCommunication

Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging

and

Appl. Sci. 2021, 11(10), 4543; https://doi.org/10.3390/app11104543 - 17 May 2021

Cited by 16 | Viewed by 1936

Abstract

This paper proposes a method to detect air pollution by applying a hyperspectral imaging algorithm for visible light, near infrared, and far infrared. By assigning hyperspectral information to images from monocular, near infrared, and thermal imaging, principal component analysis is performed on hyperspectral images taken at different times to obtain the solar radiation intensity. The Beer–Lambert law and multivariate regression analysis are used to calculate the PM2.5 and PM10 concentrations during the period, which are compared with the corresponding PM2.5 and PM10 concentrations from the Taiwan Environmental Protection Agency to evaluate the accuracy of this method. This study reveals that the accuracy in the visible light band is higher than the near-infrared and far-infrared bands, and it is also the most convenient band for data acquisition. Therefore, in the future, mobile phone cameras will be able to analyze the PM2.5 and PM10 concentrations at any given time using this algorithm by capturing images to increase the convenience and immediacy of detection. Full article

(This article belongs to the Special Issue Innovative and Advanced Applications of Hyperspectral Imaging Technology)

► Show Figures

Figure 1

Review

Jump to: Research

Open AccessReview

Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

Ivan Racetin

and

Andrija Krtalić

Appl. Sci. 2021, 11(11), 4878; https://doi.org/10.3390/app11114878 - 26 May 2021

Cited by 7 | Viewed by 2140

Abstract

Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications. Full article

(This article belongs to the Special Issue Innovative and Advanced Applications of Hyperspectral Imaging Technology)

► Show Figures