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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 March 2025) | Viewed by 19813

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Special Issue Editors

Prof. Dr. Eugene Levin

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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

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 60012, Tamil Nadu, India
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

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Published Papers (14 papers)

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19 pages, 6858 KiB

Open AccessArticle

Application Possibilities of Orthophoto Data Based on Spectral Fractal Structure Containing Boundary Conditions

by József Berke

Remote Sens. 2025, 17(7), 1249; https://doi.org/10.3390/rs17071249 - 1 Apr 2025

Viewed by 289

Abstract

The self-similar structure-based analysis of digital images offers many new practical possibilities. The fractal dimension is one of the most frequently measured parameters if we want to use image data in measurable analyses in metric spaces. In practice, the fractal dimension can be measured well in simple files containing only image data. In the case of complex image data structures defined in different metric spaces, their measurement in metric space encounters many difficulties. In this work, we provide a practical solution for the measurement of ortho-photos—as complex image data structures—based on the spectral fractal structure based on boundary conditions (height, time, and temperature), presenting the further development of the related theoretical foundations. We will discuss the optimal flight altitude determination in detail through practical examples. For this, in addition to the structural measurements on the images, we also use the well-known image entropy in information theory. The data obtained in this way can facilitate the optimal UAS operation execution that best suits further image processing tasks (e.g., classification, segmentation, and index analysis). Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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18 pages, 2362 KiB

Open AccessArticle

Hyperspectral Target Detection Based on Masked Autoencoder Data Augmentation

by Zhixuan Zhuang, Jinhui Lan and Yiliang Zeng

Remote Sens. 2025, 17(6), 1097; https://doi.org/10.3390/rs17061097 - 20 Mar 2025

Viewed by 328

Abstract

Deep metric learning combines deep learning with metric learning to explore the deep spectral space and distinguish between the target and background. Current target detection methods typically fail to accurately distinguish local differences between the target and background, leading to insufficient suppression of the pixels surrounding the target and poor detection performance. To solve this issue, a hyperspectral target detection method based on masked autoencoder data augmentation (HTD-DA) was proposed. HTD-DA includes a multi-scale spectral metric network based on a triplet network, which enhances the ability to learn local and global spectral variations using multi-scale feature extraction and feature fusion, thereby improving background suppression. To alleviate the lack of training data, a masked spectral data augmentation network was employed. It utilizes the entire hyperspectral image (HSI) training the network to learn spectral variability through mask-based reconstruction techniques and generate target samples based on the prior spectrum. Additionally, in search of more optimal spectral space, an Inter-class Difference Amplification Triplet (IDAT) Loss was introduced to enhance the separation between the target and background when finding the spectral space, by making full use of background and prior information. The experimental results demonstrated that the proposed model provides superior detection results. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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28 pages, 28459 KiB

Open AccessArticle

Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya

by Roman Shults, Ashraf Farahat, Muhammad Usman and Md Masudur Rahman

Remote Sens. 2025, 17(4), 616; https://doi.org/10.3390/rs17040616 - 11 Feb 2025

Viewed by 832

Abstract

Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, naïve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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22 pages, 24659 KiB

Open AccessArticle

A Multi-Scale Fusion Deep Learning Approach for Wind Field Retrieval Based on Geostationary Satellite Imagery

by Wei Zhang, Yapeng Wu, Kunkun Fan, Xiaojiang Song, Renbo Pang and Boyu Guoan

Remote Sens. 2025, 17(4), 610; https://doi.org/10.3390/rs17040610 - 11 Feb 2025

Viewed by 654

Abstract

Wind field retrieval, a crucial component of weather forecasting, has been significantly enhanced by recent advances in deep learning. However, existing approaches that are primarily focused on wind speed retrieval are limited by their inability to achieve real-time, full-coverage retrievals at large scales. To address this problem, we propose a novel multi-scale fusion retrieval (MFR) method, leveraging geostationary observation satellites. At the mesoscale, MFR incorporates a cloud-to-wind transformer model, which employs local self-attention mechanisms to extract detailed wind field features. At large scales, MFR incorporates a multi-encoder coordinate U-net model, which incorporates multiple encoders and utilises coordinate information to fuse meso- to large-scale features, enabling accurate and regionally complete wind field retrievals, while reducing the computational resources required. The MFR method was validated using Level 1 data from the Himawari-8 satellite, covering a geographic range of 0–60°N and 100–160°E, at a resolution of 0.25°. Wind field retrieval was accomplished within seconds using a single graphics processing unit. The mean absolute error of wind speed obtained by the MFR was 0.97 m/s, surpassing the accuracy of the CFOSAT and HY-2B Level 2B wind field products. The mean absolute error for wind direction achieved by the MFR was 23.31°, outperforming CFOSAT Level 2B products and aligning closely with HY-2B Level 2B products. The MFR represents a pioneering approach for generating initial fields for large-scale grid forecasting models. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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20 pages, 8861 KiB

Open AccessArticle

An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data

by Xiao Wang, Chunyao Yu, Xiaohong Zhang, Xue Liu, Yinxing Zhang, Junyong Fang and Qing Xiao

Remote Sens. 2025, 17(1), 55; https://doi.org/10.3390/rs17010055 - 27 Dec 2024

Viewed by 519

Abstract

Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF data and the instability of UAV platforms. These challenges stem from the diversity of LVF data bands and significant inter-band differences. Even after geometric processing, adjacent flight lines still exhibit varying degrees of geometric deformation. In this paper, a progressive grouping-based strategy for iterative band selection and registration is proposed. In addition, an improved Scale-Invariant Feature Transform (SIFT) algorithm, termed the Double Sufficiency–SIFT (DS-SIFT) algorithm, is introduced. This method first groups bands, selects the optimal reference band, and performs coarse registration based on the SIFT method. Subsequently, during the fine registration stage, it introduces an improved position/scale/orientation joint SIFT registration algorithm (IPSO-SIFT) that integrates partitioning and the principle of structural similarity. This algorithm iteratively refines registration based on the grouping results. Experimental data obtained from a self-developed and integrated LVF hyperspectral remote sensing system are utilized to verify the effectiveness of the proposed algorithm. A comparison with classical algorithms, such as SIFT and PSO-SIFT, demonstrates that the registration of LVF hyperspectral data using the proposed method achieves superior accuracy and efficiency. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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19 pages, 4696 KiB

Open AccessArticle

The Analysis of Land Use and Climate Change Impacts on Lake Victoria Basin Using Multi-Source Remote Sensing Data and Google Earth Engine (GEE)

by Maram Ali, Tarig Ali, Rahul Gawai, Lara Dronjak and Ahmed Elaksher

Remote Sens. 2024, 16(24), 4810; https://doi.org/10.3390/rs16244810 - 23 Dec 2024

Cited by 1 | Viewed by 1706

Abstract

Over 30 million people rely on Lake Victoria for survival in Northeast African countries, including Ethiopia, Eritrea, Somalia, and Djibout. The lake faces significant challenges due to changes in land use and climate. This study used multi-source remote sensing data in the Google Earth Engine (GEE) platform to create Land Use and Land Cover (LULC), land surface temperature (LST), and Normalized Difference Water Index (NDWI) layers in the period 2000–2023 to understand the impact of LULC and climate change on Lake Victoria Basin. The land use/land cover trends before 2020 indicated an increase in the urban areas from 0.13% in 2000 to 0.16% in 2020. Croplands increased from 6.51% in 2000 to 7.88% in 2020. The water surface area averaged 61,559 square km, which has increased since 2000 with an average rate of 1.3%. The “Permanent Wetland” size change from 2000 to 2020 varied from 1.70% to 1.83%. Cropland/Natural Vegetation Mosaics rose from 12.77% to 15.01%, through 2000 to 2020. However, more than 29,000 residents were displaced in mid-2020 as the water increased by 1.21 m from the fall of 2019 to the middle of 2020. Furthermore, land-surface temperature averaged 23.98 degrees in 2000 and 23.49 in 2024. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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28 pages, 12630 KiB

Open AccessArticle

Satellite Image Restoration via an Adaptive QWNNM Model

by Xudong Xu, Zhihua Zhang and M. James C. Crabbe

Remote Sens. 2024, 16(22), 4152; https://doi.org/10.3390/rs16224152 - 7 Nov 2024

Viewed by 946

Abstract

Due to channel noise and random atmospheric turbulence, retrieved satellite images are always distorted and degraded and so require further restoration before use in various applications. The latest quaternion-based weighted nuclear norm minimization (QWNNM) model, which utilizes the idea of low-rank matrix approximation and the quaternion representation of multi-channel satellite images, can achieve image restoration and enhancement. However, the QWNNM model ignores the impact of noise on similarity measurement, lacks the utilization of residual image information, and fixes the number of iterations. In order to address these drawbacks, we propose three adaptive strategies: adaptive noise-resilient block matching, adaptive feedback of residual image, and adaptive iteration stopping criterion in a new adaptive QWNNM model. Both simulation experiments with known noise/blurring and real environment experiments with unknown noise/blurring demonstrated that the effectiveness of adaptive QWNNM models outperformed the original QWNNM model and other state-of-the-art satellite image restoration models in very different technique approaches. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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15 pages, 6018 KiB

Open AccessArticle

Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images

by Xingjian Gu, Supeng Yu, Fen Huang, Shougang Ren and Chengcheng Fan

Remote Sens. 2024, 16(21), 3945; https://doi.org/10.3390/rs16213945 - 23 Oct 2024

Cited by 2 | Viewed by 1300

Abstract

Road extraction techniques based on remote sensing image have significantly advanced. Currently, fully supervised road segmentation neural networks based on remote sensing images require a significant number of densely labeled road samples, limiting their applicability in large-scale scenarios. Consequently, semi-supervised methods that utilize fewer labeled data have gained increasing attention. However, the imbalance between a small quantity of labeled data and a large volume of unlabeled data leads to local detail errors and overall cognitive mistakes in semi-supervised road extraction. To address this challenge, this paper proposes a novel consistency self-training semi-supervised method (CSSnet), which effectively learns from a limited number of labeled data samples and a large amount of unlabeled data. This method integrates self-training semi-supervised segmentation with semi-supervised classification. The semi-supervised segmentation component relies on an enhanced generative adversarial network for semantic segmentation, which significantly reduces local detail errors. The semi-supervised classification component relies on an upgraded mean-teacher network to handle overall cognitive errors. Our method exhibits excellent performance with a modest amount of labeled data. This study was validated on three separate road datasets comprising high-resolution remote sensing satellite images and UAV photographs. Experimental findings showed that our method consistently outperformed state-of-the-art semi-supervised methods and several classic fully supervised methods. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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18 pages, 3781 KiB

Open AccessArticle

Self-Attention Multiresolution Analysis-Based Informal Settlement Identification Using Remote Sensing Data

by Rizwan Ahmed Ansari and Timothy J. Mulrooney

Remote Sens. 2024, 16(17), 3334; https://doi.org/10.3390/rs16173334 - 8 Sep 2024

Cited by 1 | Viewed by 1375

Abstract

The global dilemma of informal settlements persists alongside the fast process of urbanization. Various methods for analyzing remotely sensed images to identify informal settlements using semantic segmentation have been extensively researched, resulting in the development of numerous supervised and unsupervised algorithms. Texture-based analysis is a topic extensively studied in the literature. However, it is important to note that approaches that do not utilize a multiresolution strategy are unable to take advantage of the fact that texture exists at different spatial scales. The capacity to do online mapping and precise segmentation on a vast scale while considering the diverse characteristics present in remotely sensed images carries significant consequences. This research presents a novel approach for identifying informal settlements using multiresolution analysis and self-attention techniques. The technique shows potential for being resilient in the presence of inherent variability in remotely sensed images due to its capacity to extract characteristics at many scales and prioritize areas that contain significant information. Segmented pictures underwent an accuracy assessment, where a comparison analysis was conducted based on metrics such as mean intersection over union, precision, recall, F-score, and overall accuracy. The proposed method’s robustness is demonstrated by comparing it to various state-of-the-art techniques. This comparison is conducted using remotely sensed images that have different spatial resolutions and informal settlement characteristics. The proposed method achieves a higher accuracy of approximately 95%, even when dealing with significantly different image characteristics. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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21 pages, 16543 KiB

Open AccessArticle

Bidirectional Feature Fusion and Enhanced Alignment Based Multimodal Semantic Segmentation for Remote Sensing Images

by Qianqian Liu and Xili Wang

Remote Sens. 2024, 16(13), 2289; https://doi.org/10.3390/rs16132289 - 22 Jun 2024

Cited by 1 | Viewed by 2519

Abstract

Image–text multimodal deep semantic segmentation leverages the fusion and alignment of image and text information and provides more prior knowledge for segmentation tasks. It is worth exploring image–text multimodal semantic segmentation for remote sensing images. In this paper, we propose a bidirectional feature fusion and enhanced alignment-based multimodal semantic segmentation model (BEMSeg) for remote sensing images. Specifically, BEMSeg first extracts image and text features by image and text encoders, respectively, and then the features are provided for fusion and alignment to obtain complementary multimodal feature representation. Secondly, a bidirectional feature fusion module is proposed, which employs self-attention and cross-attention to adaptively fuse image and text features of different modalities, thus reducing the differences between multimodal features. For multimodal feature alignment, the similarity between the image pixel features and text features is computed to obtain a pixel–text score map. Thirdly, we propose a category-based pixel-level contrastive learning on the score map to reduce the differences among the same category’s pixels and increase the differences among the different categories’ pixels, thereby enhancing the alignment effect. Additionally, a positive and negative sample selection strategy based on different images is explored during contrastive learning. Averaging pixel values across different training images for each category to set positive and negative samples compares global pixel information while also limiting sample quantity and reducing computational costs. Finally, the fused image features and aligned pixel–text score map are concatenated and fed into the decoder to predict the segmentation results. Experimental results on the ISPRS Potsdam, Vaihingen, and LoveDA datasets demonstrate that BEMSeg is superior to comparison methods on the Potsdam and Vaihingen datasets, with improvements in mIoU ranging from 0.57% to 5.59% and 0.48% to 6.15%, and compared with Transformer-based methods, BEMSeg also performs competitively on LoveDA dataset with improvements in mIoU ranging from 0.37% to 7.14%. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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20 pages, 12264 KiB

Open AccessArticle

Land Use Recognition by Applying Fuzzy Logic and Object-Based Classification to Very High Resolution Satellite Images

by Dario Perregrini and Vittorio Casella

Remote Sens. 2024, 16(13), 2273; https://doi.org/10.3390/rs16132273 - 21 Jun 2024

Cited by 1 | Viewed by 951

Abstract

The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broadened the scope of satellite imagery utilization across various domains that were traditionally reliant on aerial data. Our ultimate goal is to leverage this high-resolution satellite imagery to classify land use types and derive soil permeability maps by attributing permeability values to the different types of classified soil. Specifically, we aim to develop an object-based classification algorithm using fuzzy logic techniques to describe the different classes relevant to soil permeability by analyzing different test areas, and once a complete method has been developed, apply it to the entire image of Pavia. In this study area, a logical scheme was developed to classify the field classes, cultivated and uncultivated, and distinguish them from large industrial buildings, which, due to their radiometric similarity, can be classified incorrectly, especially with uncultivated fields. Validation of the classification results against ground truth data, produced by an operator manually classifying part of the image, yielded an impressive overall accuracy of 95.32%. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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26 pages, 10617 KiB

Open AccessArticle

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

by Nana Jiang, Wenbo Zhao, Hui Wang, Huiqi Luo, Zezhou Chen and Jubo Zhu

Remote Sens. 2024, 16(10), 1788; https://doi.org/10.3390/rs16101788 - 18 May 2024

Cited by 3 | Viewed by 2067

Abstract

Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of

74.68 %

in model storage requirements and

55.93 %

in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology. Full article

(This article belongs to the Special Issue Image Processing from Aerial and Satellite Imagery)

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28 pages, 11352 KiB

Open AccessArticle

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 - 1 Mar 2024

Cited by 4 | Viewed by 3311

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 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

Open AccessTechnical 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 1611

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 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 (CMSR

^{2}

) 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 (MR

^{2}

L) 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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