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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 December 2023) | Viewed by 17319

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Dr. Asanka Perera

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Autonomous System Laboratory (Building 17, Room 131), School of Engineering and IT, University of New South Wales, Canberra 2610, Australia
Interests: computer vision; robotics, mechatronics; drones; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Javaan Chahl

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Guest Editor

School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
Interests: robotics; sensing; biomimicry; aerospace
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ali Al-Naji

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Guest Editor

Electrical Engineering Technical College, Middle Technical University, Baghdad 1022, Iraq
Interests: biomedical instrumentation and sensors; health-care applications; computer vision systems; microcontroller applications

Dr. Huajian Liu

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Guest Editor

The Plant Accelerator, Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Building WT 40, Hartley Grove, Adelaide, SA 5064, Australia
Interests: machine vision and machine learning for plant phenotyping and precision agriculture; plant nutrient estimation; plant disease detection; drought and salt stress tolerance; plant growing status estimation; invertebrate pest detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital image processing techniques have evolved over the past decade at an unprecedented rate. These developments have led computer vision-based applications to become an inseparable part of our lives. Computer vision research continues to evolve and find its way into new applications. There are also many untapped research avenues.

This special issue aims at articles ranging from improving classical image processing techniques such as image representation, 2D convolutions, and frequency domain image filtering to modern approaches such as machine learning and deep learning-based solutions.

We welcome both theoretical and application solutions. Topics of interest include, but are not limited to:

Disasters - search and rescue applications, casualty detection outdoors, disaster site analysis, wildfire detection and analysis, fire detection
Aerial - aerial object detection, object tracking from drone videos, aerial mapping, drone videos, drone images, drone vision systems
Robotics - image processing for robots, vision-based localization, object detection for robots
Medical - health applications, vital signs monitoring, medical image segmentation and classification, biomedical image processing
Human motion - human action recognition, human pose recognition, human gesture recognition, face analysis
Agriculture – plant phenotyping and precision agriculture, weed and insect detection, plant condition monitoring, thermal imagery, infrared imagery and hyperspectral imaging
Image analysis - edge detection, object detection, object classification, image noise removal, image transform, optical flow, machine learning, deep learning
Biology - biologically inspired vision systems, insect vision
Datasets - image simulation, simulated image/video datasets, computer vision datasets

Dr. Asanka Perera
Prof. Dr. Javaan Chahl
Prof. Dr. Ali Al-Naji
Dr. Huajian Liu
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

computer vision
image processing
machine learning
deep learning
search and rescue
aerial images
automatic phenotyping
precision agriculture
datasets

Published Papers (12 papers)

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23 pages, 11789 KiB

Open AccessArticle

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

by Xiaopeng Li, Shuaibo Cheng, Zhaoyuan Zeng, Chen Zhao and Cien Fan

Remote Sens. 2024, 16(3), 437; https://doi.org/10.3390/rs16030437 - 23 Jan 2024

Viewed by 800

Abstract

High dynamic range imaging (HDRI) is an essential task in remote sensing, enhancing low dynamic range (LDR) remote sensing images and benefiting downstream tasks, such as object detection and image segmentation. However, conventional frame-based HDRI methods may encounter challenges in real-world scenarios due to the limited information inherent in a single image captured by conventional cameras. In this paper, an event-based remote sensing HDR imaging framework is proposed to address this problem, denoted as ERS-HDRI, which reconstructs the remote sensing HDR image from a single-exposure LDR image and its concurrent event streams. The proposed ERS-HDRI leverages a coarse-to-fine framework, incorporating the event-based dynamic range enhancement (E-DRE) network and the gradient-enhanced HDR reconstruction (G-HDRR) network. Specifically, to efficiently achieve dynamic range fusion from different domains, the E-DRE network is designed to extract the dynamic range features from LDR frames and events and perform intra- and cross-attention operations to adaptively fuse multi-modal data. A denoise network and a dense feature fusion network are then employed for the generation of the coarse, clean HDR image. Then, the G-HDRR network, with its gradient enhancement module and multiscale fusion module, performs structure enforcement on the coarse HDR image and generates a fine informative HDR image. In addition, this work introduces a specialized hybrid imaging system and a novel, real-world event-based remote sensing HDRI dataset that contains aligned remote sensing LDR images, remote sensing HDR images, and concurrent event streams for evaluation. Comprehensive experiments have demonstrated the effectiveness of the proposed method. Specifically, it improves state-of-the-art PSNR by about 30% and the SSIM score by about 9% on the real-world dataset. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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21 pages, 10780 KiB

Open AccessArticle

Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property

by Xiaobin Wu, Liangliang Zheng, Chunyu Liu, Tan Gao, Ziyu Zhang and Biao Yang

Remote Sens. 2023, 15(24), 5710; https://doi.org/10.3390/rs15245710 - 13 Dec 2023

Viewed by 721

Abstract

When a remote sensing camera work in push-broom mode, the obtained image usually contains significant stripe noise and random noise due to differences in detector response and environmental factors. Traditional approaches typically treat them as two independent problems and process the image sequentially, which not only increases the risk of information loss and structural damage, but also faces the situation of noise mutual influence. To overcome the drawbacks of traditional methods, this paper leverages the double low-rank characteristics in the underlying prior of degraded images and presents a novel approach for addressing both destriping and denoising tasks simultaneously. We utilize the commonality that both can be treated as inverse problems and place them in the same optimization framework, while designing an alternating direction method of multipliers (ADMM) strategy for solving them, achieving the synchronous removal of both stripe noise and random noise. Compared with traditional approaches, synchronous denoising technology can more accurately evaluate the distribution characteristics of noise, better utilize the original information of the image, and achieve better destriping and denoising results. To assess the efficacy of the proposed algorithm, extensive simulations and experiments were conducted in this paper. The results show that compared with state-of-the-art algorithms, the proposed method can more effectively suppress random noise, achieve better synchronous denoising results, and it exhibits a stronger robustness. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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19 pages, 10488 KiB

Open AccessArticle

VQ-InfraTrans: A Unified Framework for RGB-IR Translation with Hybrid Transformer

by Qiyang Sun, Xia Wang, Changda Yan and Xin Zhang

Remote Sens. 2023, 15(24), 5661; https://doi.org/10.3390/rs15245661 - 07 Dec 2023

Viewed by 1086

Abstract

Infrared (IR) images containing rich spectral information are essential in many fields. Most RGB-IR transfer work currently relies on conditional generative models to learn and train IR images for specific devices and scenes. However, these models only establish an empirical mapping relationship between [...] Read more.

μ

m and 8–15

μ

m) transfer task. To address this challenge, we propose VQ-InfraTrans, a comprehensive framework for transferring images from the visible spectrum to the infrared spectrum. Our framework incorporates a multi-mode approach to RGB-IR image transferring, encompassing both unconditional and conditional transfers, achieving diverse and flexible image transformations. Instead of training individual models for each specific condition or dataset, we propose a two-stage transfer framework that integrates diverse requirements into a unified model that utilizes a composite encoder–decoder based on VQ-GAN, and a multi-path transformer to translate multi-modal images from RGB to infrared. To address the issue of significant errors in transferring specific targets due to their radiance, we have developed a hybrid editing module to precisely map spectral transfer information for specific local targets. The qualitative and quantitative comparisons conducted in this work reveal substantial enhancements compared to prior algorithms, as the objective evaluation metric SSIM (structural similarity index) was improved by 2.24% and the PSNR (peak signal-to-noise ratio) was improved by 2.71%. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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20 pages, 5099 KiB

Open AccessArticle

Feature Relation Guided Cross-View Image Based Geo-Localization

by Qingfeng Hou, Jun Lu, Haitao Guo, Xiangyun Liu, Zhihui Gong, Kun Zhu and Yifan Ping

Remote Sens. 2023, 15(20), 5029; https://doi.org/10.3390/rs15205029 - 19 Oct 2023

Viewed by 946

Abstract

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-localization algorithm proposed in this study yields results of 92.08%, 97.70%, and 98.66% for the top 1, top 5, and top 10 metrics, respectively, in CVUSA, a popular public cross-view dataset, and exhibits superior performance compared to algorithms of the same type. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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18 pages, 5301 KiB

Open AccessArticle

Blind Restoration of a Single Real Turbulence-Degraded Image Based on Self-Supervised Learning

by Yiming Guo, Xiaoqing Wu, Chun Qing, Liyong Liu, Qike Yang, Xiaodan Hu, Xianmei Qian and Shiyong Shao

Remote Sens. 2023, 15(16), 4076; https://doi.org/10.3390/rs15164076 - 18 Aug 2023

Viewed by 842

Abstract

Turbulence-degraded image frames are distorted by both turbulent deformations and space–time varying blurs. Restoration of the atmospheric turbulence-degraded image is of great importance in the state of affairs, such as remoting sensing, surveillance, traffic control, and astronomy. While traditional supervised learning uses lots of simulated distorted images for training, it has poor generalization ability for real degraded images. To address this problem, a novel blind restoration network that only inputs a single turbulence-degraded image is presented, which is mainly used to reconstruct the real atmospheric turbulence distorted images. In addition, the proposed method does not require pre-training, and only needs to input a single real turbulent degradation image to output a high-quality result. Meanwhile, to improve the self-supervised restoration effect, Regularization by Denoising (RED) is introduced to the network, and the final output is obtained by averaging the prediction of multiple iterations in the trained model. Experiments are carried out with real-world turbulence-degraded data by implementing the proposed method and four reported methods, and we use four non-reference indicators for evaluation, among which Average Gradient, NIQE, and BRISQUE have achieved state-of-the-art effects compared with other methods. As a result, our method is effective in alleviating distortions and blur, restoring image details, and enhancing visual quality. Furthermore, the proposed approach has a certain degree of generalization, and has an excellent restoration effect for motion-blurred images. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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23 pages, 6541 KiB

Open AccessArticle

LMDFS: A Lightweight Model for Detecting Forest Fire Smoke in UAV Images Based on YOLOv7

by Gong Chen, Renxi Cheng, Xufeng Lin, Wanguo Jiao, Di Bai and Haifeng Lin

Remote Sens. 2023, 15(15), 3790; https://doi.org/10.3390/rs15153790 - 30 Jul 2023

Cited by 11 | Viewed by 2379

Abstract

Forest fires pose significant hazards to ecological environments and economic society. The detection of forest fire smoke can provide crucial information for the suppression of early fires. Previous detection models based on deep learning have been limited in detecting small smoke and smoke with smoke-like interference. In this paper, we propose a lightweight model for forest fire smoke detection that is suitable for UAVs. Firstly, a smoke dataset is created from a combination of forest smoke photos obtained through web crawling and enhanced photos generated by using the method of synthesizing smoke. Secondly, the GSELAN and GSSPPFCSPC modules are built based on Ghost Shuffle Convolution (GSConv), which efficiently reduces the number of parameters in the model and accelerates its convergence speed. Next, to address the problem of indistinguishable feature boundaries between clouds and smoke, we integrate coordinate attention (CA) into the YOLO feature extraction network to strengthen the extraction of smoke features and attenuate the background information. Additionally, we use Content-Aware Reassembly of FEatures (CARAFE) upsampling to expand the receptive field in the feature fusion network and fully exploit the semantic information. Finally, we adopt SCYLLA-Intersection over Union (SIoU) loss as a replacement for the original loss function in the prediction phase. This substitution leads to improved convergence efficiency and faster convergence. The experimental results demonstrate that the LMDFS model proposed for smoke detection achieves an accuracy of 80.2% with a 5.9% improvement compared to the baseline and a high number of Frames Per Second (FPS)—63.4. The model also reduces the parameter count by 14% and Giga FLoating-point Operations Per second (GFLOPs) by 6%. These results suggest that the proposed model can achieve a high accuracy while requiring fewer computational resources, making it a promising approach for practical deployment in applications for detecting smoke. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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16 pages, 1092 KiB

Open AccessArticle

Depth Information Precise Completion-GAN: A Precisely Guided Method for Completing Ill Regions in Depth Maps

by Ren Qian, Wenfeng Qiu, Wenbang Yang, Jianhua Li, Yun Wu, Renyang Feng, Xinan Wang and Yong Zhao

Remote Sens. 2023, 15(14), 3686; https://doi.org/10.3390/rs15143686 - 24 Jul 2023

Viewed by 850

Abstract

In the depth map obtained through binocular stereo matching, there are many ill regions due to reasons such as lighting or occlusion. These ill regions cannot be accurately obtained due to the lack of information required for matching. Since the completion model based on Gan generates random results, it cannot accurately complete the depth map. Therefore, it is necessary to accurately complete the depth map according to reality. To address this issue, this paper proposes a depth information precise completion GAN (DIPC-GAN) that effectively uses the Guid layer normalization (GuidLN) module to guide the model for precise completion by utilizing depth edges. GuidLN flexibly adjusts the weights of the guiding conditions based on intermediate results, allowing modules to accurately and effectively incorporate the guiding information. The model employs multiscale discriminators to discriminate results of different resolutions at different generator stages, enhancing the generator’s grasp of overall image and detail information. Additionally, this paper proposes Attention-ResBlock, which enables all ResBlocks in each task module of the GAN-based multitask model to focus on their own task by sharing a mask. Even when the ill regions are large, the model can effectively complement the missing details in these regions. Additionally, the multiscale discriminator in the model enhances the generator’s robustness. Finally, the proposed task-specific residual module can effectively focus different subnetworks of a multitask model on their respective tasks. The model has shown good repair results on datasets, including artificial, real, and remote sensing images. The final experimental results showed that the model’s REL and RMSE decreased by 9.3% and 9.7%, respectively, compared to RDFGan. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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15 pages, 2098 KiB

Open AccessCommunication

Coastal Ship Tracking with Memory-Guided Perceptual Network

by Xi Yang, Haiyang Zhu, Hua Zhao and Dong Yang

Remote Sens. 2023, 15(12), 3150; https://doi.org/10.3390/rs15123150 - 16 Jun 2023

Viewed by 851

Abstract

Coastal ship tracking is used in many applications, such as autonomous navigation, maritime rescue, and environmental monitoring. Many general object-tracking methods based on deep learning have been explored for ship tracking, but they often fail to accurately track ships in challenging scenarios, such as occlusion, scale variation, and motion blur. We propose a memory-guided perception network (MGPN) to address these issues. MGPN has two main innovative improvements. The dynamic memory mechanism (DMM) in the proposed method stores past features of the tracked target to enhance the model’s feature fusion capability in the temporal dimension. Meanwhile, the hierarchical context-aware module (HCAM) enables the interaction of different scales, global and local information, to address the scale discrepancy of targets and improve the feature fusion capability in the spatial dimension. These innovations enhance the robustness of tracking and reduce inaccuracies in the bounding boxes. We conducted an in-depth ablation study to demonstrate the effectiveness of DMM and HCAM. Finally, influenced by the above two points, MGPN has achieved state-of-the-art performance on a large offshore ship tracking dataset, which contains challenging scenarios such as complex backgrounds, ship occlusion, and varying scales. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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19 pages, 4746 KiB

Open AccessArticle

An Efficient Cloud Classification Method Based on a Densely Connected Hybrid Convolutional Network for FY-4A

by Bo Wang, Mingwei Zhou, Wei Cheng, Yao Chen, Qinghong Sheng, Jun Li and Li Wang

Remote Sens. 2023, 15(10), 2673; https://doi.org/10.3390/rs15102673 - 21 May 2023

Cited by 3 | Viewed by 1276

Abstract

Understanding atmospheric motions and projecting climate changes depends significantly on cloud types, i.e., different cloud types correspond to different atmospheric conditions, and accurate cloud classification can help forecasts and meteorology-related studies to be more effectively directed. However, accurate classification of clouds is challenging and often requires certain manual involvement due to the complex cloud forms and dispersion. To address this challenge, this paper proposes an improved cloud classification method based on a densely connected hybrid convolutional network. A dense connection mechanism is applied to hybrid three-dimensional convolutional neural network (3D-CNN) and two-dimensional convolutional neural network (2D-CNN) architectures to use the feature information of the spatial and spectral channels of the FY-4A satellite fully. By using the proposed network, cloud categorization solutions with a high temporal resolution, extensive coverage, and high accuracy can be obtained without the need for any human intervention. The proposed network is verified using tests, and the results show that it can perform real-time classification tasks for seven different types of clouds and clear skies in the Chinese region. For the CloudSat 2B-CLDCLASS product as a test target, the proposed network can achieve an overall accuracy of 95.2% and a recall of more of than 82.9% for all types of samples, outperforming the other deep-learning-based techniques. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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21 pages, 7266 KiB

Open AccessArticle

Assessment of Machine and Deep Learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography

by Sahbi Boubaker, Souad Kamel, Nejib Ghazouani and Adel Mellit

Remote Sens. 2023, 15(6), 1686; https://doi.org/10.3390/rs15061686 - 21 Mar 2023

Cited by 11 | Viewed by 2444

Abstract

Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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15 pages, 5045 KiB

Open AccessTechnical Note

Aircraft Target Detection in Low Signal-to-Noise Ratio Visible Remote Sensing Images

by Ruize Niu, Xiyang Zhi, Shikai Jiang, Jinnan Gong, Wei Zhang and Lijian Yu

Remote Sens. 2023, 15(8), 1971; https://doi.org/10.3390/rs15081971 - 08 Apr 2023

Cited by 6 | Viewed by 1772

Abstract

With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm’s robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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17 pages, 4995 KiB

Open AccessTechnical Note

A Method for Detecting Feature-Sparse Regions and Matching Enhancement

by Longhao Wang, Chaozhen Lan, Beibei Wu, Tian Gao, Zijun Wei and Fushan Yao

Remote Sens. 2022, 14(24), 6214; https://doi.org/10.3390/rs14246214 - 08 Dec 2022

Cited by 1 | Viewed by 1182

Abstract

Image matching is a key research issue in the intelligent processing of remote sensing images. Due to the large phase differences or apparent differences in ground features between unmanned aerial vehicle imagery and satellite imagery, as well as the large number of sparsely textured areas, image matching between the two types of imagery is very difficult. Tackling the difficult problem of matching unmanned aerial vehicle imagery and satellite imagery, a feature sparse region detection and matching enhancement algorithm (SD-ME) is proposed in this study. First, the SuperGlue algorithm was used to initially match the two images, and feature-sparse region detection was performed with the help of the image features and initial matching results, with the detected feature sparse areas stored in a linked list one by one. Then, according to the order of storage, feature re-extraction was performed on the feature-sparse areas individually, and an adaptive threshold feature screening algorithm was proposed to filter and screen the re-extracted features. This retains only high-confidence features in the region and improves the reliability of matching enhancement results. Finally, local features with high scores that were re-extracted in the feature-sparse areas were aggregated and input to the SuperGlue network for matching, and thus, reliable matching enhancement results were obtained. The experiment selected four pairs of un-manned aerial vehicle imagery and satellite imagery that were difficult to match and compared the SD-ME algorithm with the SIFT, ContextDesc, and SuperGlue algorithms. The results revealed that the SD-ME algorithm was far superior to other algorithms in terms of the number of correct matching points, the accuracy of matching points, and the uniformity of distribution of matching points. The number of correctly matched points in each image pair increased by an average of 95.52% compared to SuperGlue. The SD-ME algorithm can effectively improve the matching quality between unmanned aerial vehicle imagery and satellite imagery and has practical value in the fields of image registration and change detection. Full article

(This article belongs to the Special Issue Computer Vision and Image Processing in Remote Sensing)

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