Special Issue "Digital Image Processing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Przemysław Kupidura
E-Mail Website
Guest Editor
Warsaw University of Technology
Interests: remote sensing; digital image processing; mathematical morphology
Dr. Joanna Pluto-Kossakowska
E-Mail Website
Guest Editor
Warsaw University of Technology
Interests: remote sensing; signal processing

Special Issue Information

Dear colleagues,

In today's world of advanced photogrammetry and remote sensing, almost all data are created and stored in digital form. Digital image processing is therefore present, to a varying degree, at every stage of remote sensing data analysis: geometric and radiometric correction, filtration, image enhancement, interpretation, and extraction of information. On the one hand, methods to improve the process to create reliable information from remote sensing data should be regarded as a great opportunity. On the other hand, they should be regarded as a necessity: Every day, terabytes of imagery and other types of remote sensing data are created. To fully exploit this potential, we need digital processing methods of high efficiency, but also fast and with the highest degree of automation.

This proposed Special Issue addresses research on digital image processing methods, e.g., their new applications, increasing their efficacy and efficiency. We invite you to present research on various aspects of image processing: machine learning, object-based analysis, filtration, image enhancement, atmospheric correction, texture analysis, and others in application on various types of remote sensing data: optical, radar, and laser scanning data.

Prof. Przemysław Kupidura
Dr. Joanna Pluto-Kossakowska
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 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. Remote Sensing 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

  • Digital image processing
  • Radiometric correction
  • Atmospheric correction
  • Filtering
  • Machine learning
  • Texture analysis
  • GEOBIA

Published Papers (2 papers)

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Research

Article
CONIC: Contour Optimized Non-Iterative Clustering Superpixel Segmentation
Remote Sens. 2021, 13(6), 1061; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061061 - 11 Mar 2021
Viewed by 445
Abstract
Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel [...] Read more.
Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel Contour Optimized Non-Iterative Clustering (CONIC) method is presented. It incorporates contour prior into the non-iterative clustering framework, aiming to provide a balanced trade-off between segmentation accuracy and visual uniformity. After the conventional grid sampling initialization, a regional inter-seed correlation is first established by the joint color-spatial-contour distance. It then guides a global redistribution of all seeds to modify the number and positions iteratively. This is done to avoid clustering falling into the local optimum and achieve the exact number of user-expectation. During the clustering process, an improved feature distance is elaborated to measure the color similarity that considers contour constraint and prevents the boundary pixels from being wrongly assigned. Consequently, superpixels acquire better visual quality and their boundaries are more consistent with the object contours. Experimental results show that CONIC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms, in terms of both efficiency and segmentation effects. Full article
(This article belongs to the Special Issue Digital Image Processing)
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Article
A 117 Line 2D Digital Image Correlation Code Written in MATLAB
Remote Sens. 2020, 12(18), 2906; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12182906 - 08 Sep 2020
Cited by 4 | Viewed by 1304 | Correction
Abstract
Digital Image Correlation (DIC) has become a popular tool in many fields to determine the displacements and deformations experienced by an object from images captured of the object. Although there are several publications which explain DIC in its entirety while still catering to [...] Read more.
Digital Image Correlation (DIC) has become a popular tool in many fields to determine the displacements and deformations experienced by an object from images captured of the object. Although there are several publications which explain DIC in its entirety while still catering to newcomers to the concept, these publications neglect to discuss how the theory presented is implemented in practice. This gap in literature, which this paper aims to address, makes it difficult to gain a working knowledge of DIC, which is necessary in order to contribute towards its development. The paper attempts to address this by presenting the theory of a 2D, subset-based DIC framework that is predominantly consistent with state-of-the-art techniques, and discussing its implementation as a modular MATLAB code. The correlation aspect of this code is validated, showing that it performs on par with well-established DIC algorithms and thus is sufficiently reliable for practical use. This paper, therefore, serves as an educational resource to bridge the gap between the theory of DIC and its practical implementation. Furthermore, although the code is designed as an educational resource, its validation combined with its modularity makes it attractive as a starting point to develop the capabilities of DIC. Full article
(This article belongs to the Special Issue Digital Image Processing)
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