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Remote Sensing
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Digital Image Processing
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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: closed (15 April 2022) | Viewed by 14407

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 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. 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 2700 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 (4 papers)

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Research

20 pages, 2662 KiB  
Article
Vector Fuzzy c-Spherical Shells (VFCSS) over Non-Crisp Numbers for Satellite Imaging
by Iman Abaspur Kazerouni, Hadi Mahdipour, Gerard Dooly and Daniel Toal
Remote Sens. 2021, 13(21), 4482; https://doi.org/10.3390/rs13214482 - 08 Nov 2021
Cited by 2 | Viewed by 1496
Abstract
The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster shells involving non-crisp numbers, in this paper. This is achieved by a vectorized representation of distance, between two non-crisp numbers like the crisp numbers case. Using the proposed clustering method, named [...] Read more.
The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster shells involving non-crisp numbers, in this paper. This is achieved by a vectorized representation of distance, between two non-crisp numbers like the crisp numbers case. Using the proposed clustering method, named vector fuzzy c-spherical shells (VFCSS), all crisp and non-crisp numbers can be clustered by the FCSS algorithm in a unique structure. Therefore, we can implement FCSS clustering over various types of numbers in a unique structure with only a few alterations in the details used in implementing each case. The relations of VFCSS applied to crisp and non-crisp (containing symbolic-interval, LR-type, TFN-type and TAN-type fuzzy) numbers are presented in this paper. Finally, simulation results are reported for VFCSS applied to synthetic LR-type fuzzy numbers; where the application of the proposed method in real life and in geomorphology science is illustrated by extracting the radii of circular agricultural fields using remotely sensed images and the results show better performance and lower cost computational complexity of the proposed method in comparison to conventional FCSS. Full article
(This article belongs to the Special Issue Digital Image Processing)
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21 pages, 16915 KiB  
Article
A Flexible Region of Interest Extraction Algorithm with Adaptive Threshold for 3-D Synthetic Aperture Radar Images
by Liang Li, Xiaoling Zhang, Bokun Tian, Chen Wang, Liming Pu, Jun Shi and Shunjun Wei
Remote Sens. 2021, 13(21), 4308; https://doi.org/10.3390/rs13214308 - 26 Oct 2021
Cited by 1 | Viewed by 1578
Abstract
Most of the existing image segmentation methods have a strong anti-noise ability but are susceptible to the interference in the background, so they are not suitable for 3-D synthetic aperture radar (SAR) image target extraction. Region of interest (ROI) extraction can improve the [...] Read more.
Most of the existing image segmentation methods have a strong anti-noise ability but are susceptible to the interference in the background, so they are not suitable for 3-D synthetic aperture radar (SAR) image target extraction. Region of interest (ROI) extraction can improve the anti-interference ability of the image segmentation methods. However, the existing ROI extraction method uses the same threshold to process all the images in the data set. This threshold is not optimal for each image. Designed for 3-D SAR image target extraction, we propose an ROI extraction algorithm with adaptive threshold (REAT) to enhance the anti-interference ability of the existing image segmentation methods. The required thresholds in the proposed algorithm are adaptively obtained by the mapping of the image features. Moreover, the proposed algorithm can easily be applied to existing image segmentation methods. The experiments demonstrate that the proposed algorithm significantly enhances the anti-interference ability and computational efficiency of the image segmentation methods. Compared with the existing ROI extraction algorithm, the proposed algorithm improves the dice similarity coefficient by 6.4%. Full article
(This article belongs to the Special Issue Digital Image Processing)
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23 pages, 11389 KiB  
Article
CONIC: Contour Optimized Non-Iterative Clustering Superpixel Segmentation
by Cheng Li, Baolong Guo, Nannan Liao, Jianglei Gong, Xiaodong Han, Shuwei Hou, Zhijie Chen and Wangpeng He
Remote Sens. 2021, 13(6), 1061; https://doi.org/10.3390/rs13061061 - 11 Mar 2021
Cited by 3 | Viewed by 2178
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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31 pages, 3614 KiB  
Article
A 117 Line 2D Digital Image Correlation Code Written in MATLAB
by Devan Atkinson and Thorsten Becker
Remote Sens. 2020, 12(18), 2906; https://doi.org/10.3390/rs12182906 - 08 Sep 2020
Cited by 23 | Viewed by 6851 | 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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