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Electronics
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Remote Sensing Image Processing
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Remote Sensing Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 8124

Special Issue Editors

Prof. Giovanni Ramponi

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Trieste, Via A. Valerio 10, I-34127 Trieste, Italy
Interests: image and video processing, analysis, understanding
Prof. Dr. Raffaela Cefalo

E-Mail Website
Co-Guest Editor
GeoSNAv Lab, Department of Engineering and Architecture, University of Trieste, via A. Valerio 6/2, I-34127 Trieste, Italy
Interests: satellite geodesy and navigation; GNSS; topography; cartography; photogrammetry; GIS; remote sensing; ICT
Prof. Dr. Žiga Kokalj

E-Mail Website
Co-Guest Editor
1. Research Centre of the Slovenian Academy of Sciences and Arts, Novi trg 2, SI-1000 Ljubljana, Slovenia
2. Centre of Excellence for Space sciences and technologies, Aškerčeva 12, SI-1000 Ljubljana, Slovenia
Interests: remote sensing; image processing; geographic information systems; lidar data processing; environment protection

Special Issue Information

Dear Colleagues, 

Despite the ever-increasing performance of modern acquisition equipment and algorithms for processing raw remote sensing data, there are still some glitches and problems to deal with in the image data domain.

In particular, atmospheric effects can be a difficult problem due to their variability and nonuniformity, even in different parts of a given image. Other image-quality issues are related to image resolution depending on the level of detail of the acquired scene: in particular, super-resolution and data fusion are still open problems.

Defining quality itself is an issue: standard signal-to-noise-based metrics require an ideal reference image, which often is not available. Tools for referenceless analysis need to be tuned to the type of images being evaluated. Furthermore, most of the available quality metrics are not reliable estimators of quality as perceived by a human user or of the performance of an automated analysis tool.

The applications of remote-sensing imagery in the fields of environmental characterization, land consumption, and disaster management are extremely important and have a high potential for further development.

The proposed Special Issue is dedicated to providing an up-to-date panorama of recent advances in these fields, taking into account the computational and energy consumption requirements of the proposed solutions. In this way, the reader will establish a link between the methods, the processing techniques, and the corresponding application domain.

Prof. Giovanni Ramponi
Prof. Dr. Raffaela Cefalo
Prof. Dr. Žiga Kokalj
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. Electronics 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

  • remote sensing
  • image processing
  • image fusion
  • processing architectures
  • GIS (geographic information system)
  • GNSS (global navigation satellite system)
  • spatial data
  • geometric distortions
  • land consumption
  • dimensionality reduction

Published Papers (2 papers)

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Research

15 pages, 2945 KiB  
Article
Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN
by Chayma Bahhar, Amel Ksibi, Manel Ayadi, Mona M. Jamjoom, Zahid Ullah, Ben Othman Soufiene and Hedi Sakli
Electronics 2023, 12(1), 228; https://doi.org/10.3390/electronics12010228 - 2 Jan 2023
Cited by 21 | Viewed by 5807
Abstract
One of the most expensive and fatal natural disasters in the world is forest fires. For this reason, early discovery of forest fires helps minimize mortality and harm to ecosystems and forest life. The present research enriches the body of knowledge by evaluating [...] Read more.
One of the most expensive and fatal natural disasters in the world is forest fires. For this reason, early discovery of forest fires helps minimize mortality and harm to ecosystems and forest life. The present research enriches the body of knowledge by evaluating the effectiveness of an efficient wildfire and smoke detection solution implementing ensembles of multiple convolutional neural network architectures tackling two different computer vision tasks in a stage format. The proposed architecture combines the YOLO architecture with two weights with a voting ensemble CNN architecture. The pipeline works in two stages. If the CNN detects the existence of abnormality in the frame, then the YOLO architecture localizes the smoke or fire. The addressed tasks are classification and detection in the presented method. The obtained model’s weights achieve very decent results during training and testing. The classification model achieves a 0.95 F1-score, 0.99 accuracy, and 0.98e sensitivity. The model uses a transfer learning strategy for the classification task. The evaluation of the detector model reveals strong results by achieving a 0.85 mean average precision with 0.5 threshold ([email protected]) score for the smoke detection model and 0.76 mAP for the combined model. The smoke detection model also achieves a 0.93 F1-score. Overall, the presented deep learning pipeline shows some important experimental results with potential implementation capabilities despite some issues encountered during training, such as the lack of good-quality real-world unmanned aerial vehicle (UAV)-captured fire and smoke images. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing)
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23 pages, 8166 KiB  
Article
Super-Resolution Reconstruction Model of Spatiotemporal Fusion Remote Sensing Image Based on Double Branch Texture Transformers and Feedback Mechanism
by Hui Liu, Yurong Qian, Guangqi Yang and Hao Jiang
Electronics 2022, 11(16), 2497; https://doi.org/10.3390/electronics11162497 - 10 Aug 2022
Cited by 2 | Viewed by 1705
Abstract
High spatial-temporal resolution plays a vital role in the application of geoscience dynamic observance and prediction. However, thanks to the constraints of technology and budget, it is troublesome for one satellite detector to get high spatial-temporal resolution remote sensing images. Individuals have developed [...] Read more.
High spatial-temporal resolution plays a vital role in the application of geoscience dynamic observance and prediction. However, thanks to the constraints of technology and budget, it is troublesome for one satellite detector to get high spatial-temporal resolution remote sensing images. Individuals have developed spatiotemporal image fusion technology to resolve this downside, and deep remote sensing images with spatiotemporal resolution have become a possible and efficient answer. Due to the fixed size of the receptive field of convolutional neural networks, the features extracted by convolution operations cannot capture long-range features, so the correlation of global features cannot be modeled in the deep learning process. We propose a spatiotemporal fusion model of remote sensing images to solve these problems based on a dual branch feedback mechanism and texture transformer. The model separates the network from the coarse-fine images with similar structures through the idea of double branches and reduces the dependence of images on time series. It principally merges the benefits of transformer and convolution network and employs feedback mechanism and texture transformer to extract additional spatial and temporal distinction features. The primary function of the transformer module is to learn global temporal correlations and fuse temporal features with spatial features. To completely extract additional elaborated features in several stages, we have a tendency to design a feedback mechanism module. This module chiefly refines the low-level representation through high-level info and obtains additional elaborated features when considering the temporal and spacial characteristics. We have a tendency to receive good results by comparison with four typical spatiotemporal fusion algorithms, proving our model’s superiority and robustness. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing)
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