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Remote Sensing
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Image Processing from Aerial and Satellite Imagery
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Image Processing from Aerial and Satellite Imagery

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 2024 | Viewed by 2878

Special Issue Editors

Prof. Dr. Eugene Levin

E-Mail Website
Guest Editor
School of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37208, USA
Interests: geospatial big data to support health care related application scenarios; unmanned aerial systems for environmental monitoring and emergence situations response; close-range photogrammetry, computer vision and 3D printing for health care and epidemiology; human–computer/human–robot symbiosis for decision support systems
Prof. Dr. Roman Shults

E-Mail Website
Guest Editor
Interdisciplinary Research Center for Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Interests: machine learning and artificial intelligence models; approaches to remote sensing applications and geospatial data processing Innovative remote sensing and photogrammetry technologies for the assessment of the environmental impact of construction; solving problems of town planning and spatial territorial management; research into and application of remote sensing, UAS, close-range photogrammetry, and terrestrial laser scanning
Dr. Surya Prakash Tiwari

E-Mail Website
Guest Editor
Applied Research Center for Environment and Marine Studies, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Interests: remote sensing for aquatic and land applications; earth observations; image processing; ocean optics; radiative transfer theory; modelling water quality features using machine learning; object detection using remote sensing and AI-based approaches; land capability, coastal vulnerability, and environmental sensitivity mapping for decision support

Special Issue Information

Dear Colleagues,

Aerial and satellite imagery are invaluable resources in various fields, including environmental monitoring, urban planning, agriculture, disaster management, and more. This Special Issue of Remote Sensing, entitled “Image Processing from Aerial and Satellite Imagery”, aims to bring together cutting-edge interdisciplinary research in image processing techniques, geospatial science, and technology tailored to these data sources. With the proliferation of remote sensing platforms, there is a growing need for the use of advanced image analysis methods to extract meaningful information from the vast volumes of aerial and satellite imagery available today.

The primary objective of this Special Issue is to provide a platform for researchers, scientists, and experts that allows them to share their latest findings and innovations in the field of image processing for aerial and satellite imagery. This research aligns seamlessly with the journal's scope, which focuses on remote sensing technologies and their applications. By fostering collaboration and knowledge exchange, this Special Issue seeks to advance state-of-the-art image processing techniques, geospatial information science, and technologies for real-world applications, including challenges associated with the deployment of the geospatial big data obtained using satellite-, aerial/UAV-, and terrestrial-based observation techniques of Earth observation.

We invite submissions of original research articles, reviews, and innovative methodologies addressing, but not limited to, the following themes:

  • Image enhancement and restoration: Novel approaches for improving the quality of aerial and satellite images, including noise reduction, deblurring, correction, and super-resolution.
  • Feature extraction and classification: Algorithms and methods for automated detection and classification of objects and phenomena in imagery, such as land use/land cover classification, object recognition, and change detection.
  • Machine learning and deep learning: Applications of machine learning and deep learning techniques for image analysis in remote sensing, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
  • Data fusion: Integration of multiple data sources, such as multispectral, hyperspectral, and LiDAR data, to enhance the information extracted from imagery.
  • Time series analysis: Temporal analysis of aerial and satellite imagery to monitor dynamic processes and long-term trends.
  • Applications: Real-world applications of aerial and satellite imagery processing in fields like agriculture, forestry, urban planning, disaster monitoring, and environmental conservation.

This Special Issue provides a unique opportunity for researchers to contribute to the advancement of image processing methods for aerial and satellite imagery, ultimately supporting informed decision making and sustainable development in a variety of domains. We encourage authors to submit their high-quality research in order to help shape the future of this critical research area.

Prof. Dr. Eugene Levin
Prof. Dr. Roman Shults
Dr. Surya Prakash Tiwari
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

  • remote sensing
  • aerial imagery
  • satellite imagery
  • image processing
  • data fusion
  • machine learning
  • feature extraction
  • change detection
  • environmental monitoring
  • photogrammetry/space photogrammetry
  • land use/land cover classification
  • geospatial analysis

Published Papers (2 papers)

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28 pages, 11352 KiB  
Article
Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network
by Sourav Modak, Jonathan Heil and Anthony Stein
Remote Sens. 2024, 16(5), 874; https://doi.org/10.3390/rs16050874 - 01 Mar 2024
Viewed by 1660
Abstract
Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images [...] Read more.
Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues. Full article
(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)
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15 pages, 2538 KiB  
Technical Note
Multi-Scale Image- and Feature-Level Alignment for Cross-Resolution Person Re-Identification
by Guoqing Zhang, Zhun Wang, Jiangmei Zhang, Zhiyuan Luo and Zhihao Zhao
Remote Sens. 2024, 16(2), 278; https://doi.org/10.3390/rs16020278 - 10 Jan 2024
Viewed by 684
Abstract
Cross-Resolution Person Re-Identification (re-ID) aims to match images with disparate resolutions arising from variations in camera hardware and shooting distances. Most conventional works utilize Super-Resolution (SR) models to recover Low Resolution (LR) images to High Resolution (HR) images. However, because the SR models [...] Read more.
Cross-Resolution Person Re-Identification (re-ID) aims to match images with disparate resolutions arising from variations in camera hardware and shooting distances. Most conventional works utilize Super-Resolution (SR) models to recover Low Resolution (LR) images to High Resolution (HR) images. However, because the SR models cannot completely compensate for the missing information in the LR images, there is still a large gap between the HR image recovered from the LR images and the real HR images. To tackle this challenge, we propose a novel Multi-Scale Image- and Feature-Level Alignment (MSIFLA) framework to align the images on multiple resolution scales at both the image and feature level. Specifically, (i) we design a Cascaded Multi-Scale Resolution Reconstruction (CMSR2) module, which is composed of three cascaded Image Reconstruction (IR) networks, and can continuously reconstruct multiple variables of different resolution scales from low to high for each image, regardless of image resolution. The reconstructed images with specific resolution scales are of similar distribution; therefore, the images are aligned on multiple resolution scales at the image level. (ii) We propose a Multi-Resolution Representation Learning (MR2L) module which consists of three-person re-ID networks to encourage the IR models to preserve the ID-discriminative information during training separately. Each re-ID network focuses on mining discriminative information from a specific scale without the disturbance from various resolutions. By matching the extracted features on three resolution scales, the images with different resolutions are also aligned at the feature-level. We conduct extensive experiments on multiple public cross-resolution person re-ID datasets to demonstrate the superiority of the proposed method. In addition, the generalization of MSIFLA in handling cross-resolution retrieval tasks is verified on the UAV vehicle dataset. Full article
(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)
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