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Emerging Remote Sensing Techniques and Applications for Object Detection
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Emerging Remote Sensing Techniques and Applications for Object Detection

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 9503

Special Issue Editors

Dr. Yexin Wang

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: planetary remote sensing; artificial intelligence and pattern recognition; image processing and 3D measurement
Dr. Yi Cui

E-Mail Website
Guest Editor
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
Interests: image processing; electro-optical imaging; object detection

Special Issue Information

Dear Colleagues,

Object detection is a fundamental but challenging problem in the field of remote sensing. With different types of data sources, such as UAVs, airplanes, satellites, spacecraft, etc., it has a wide range of applications, such as environmental monitoring, dynamic object monitoring, geological hazard detection, land-use/land-cover mapping, change detection, geographic information system update, precision agriculture, urban planning, landing site selection and estimation, exploration planning, etc.

The latest research on this area has been making great progress in many directions. With the continuous improvement of the hardware conditions and the gradual innovation of image processing technology, new techniques, methods and applications for object detection have emerged in the field of remote sensing in recent years.

This Special Issue aims to bring together researchers from academia, industry, and government agencies to understand the innovative technologies in the field of object detection in remote sensing. Submitted papers are expected to employ state-of-the-art and novel approaches to cover solutions for object detection related, but not limited, to the following topics:

  • Innovative theories and approaches for object detection and its applications using remote sensing data such as optical images, laser, SAR data, etc.;
  • Object detection methods and applications using remote sensing data captured using UAVs, airplanes, satellites, spacecraft, etc.;
  • Fusion of multi-sensor data for object detection;
  • Deep learning for object detection, image classification, and semantic and instance segmentation;
  • Supervised, weakly supervised, and unsupervised machine learning for object detection using remote sensing data;
  • Imbalance problem (classes, scales, spatial, and objectives) solutions;
  • Object detection in challenging conditions;
  • Transfer learning, and deep reinforcement learning for object detection using remote sensing data.

Dr. Yexin Wang
Dr. Yi Cui
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. Sensors 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 2600 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
  • object detection
  • emerging techniques
  • deep learning
  • segmentation
  • satellite image

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Published Papers (5 papers)

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Research

16 pages, 11407 KiB  
Article
YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang’e-6 Landing Area
by Jing Nan, Yexin Wang, Kaichang Di, Bin Xie, Chenxu Zhao, Biao Wang, Shujuan Sun, Xiangjin Deng, Hong Zhang and Ruiqing Sheng
Sensors 2025, 25(1), 243; https://doi.org/10.3390/s25010243 - 3 Jan 2025
Cited by 1 | Viewed by 1452
Abstract
The Chang’e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole–Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe [...] Read more.
The Chang’e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole–Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model’s ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km × 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies. Full article
(This article belongs to the Special Issue Emerging Remote Sensing Techniques and Applications for Object Detection)
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33 pages, 57153 KiB  
Article
Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery
by Laith A. H. Al-Shimaysawee, Anthony Finn, Delene Weber, Morgan F. Schebella and Russell S. A. Brinkworth
Sensors 2024, 24(21), 7048; https://doi.org/10.3390/s24217048 - 31 Oct 2024
Viewed by 1217
Abstract
Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, [...] Read more.
Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala’s heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community. Full article
(This article belongs to the Special Issue Emerging Remote Sensing Techniques and Applications for Object Detection)
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18 pages, 6947 KiB  
Article
Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks
by Shen Cao, Congxia Zhao, Jian Dong and Xiongjun Fu
Sensors 2024, 24(13), 4290; https://doi.org/10.3390/s24134290 - 1 Jul 2024
Cited by 1 | Viewed by 1820
Abstract
Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result [...] Read more.
Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method. Full article
(This article belongs to the Special Issue Emerging Remote Sensing Techniques and Applications for Object Detection)
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19 pages, 15169 KiB  
Article
Urban Pedestrian Routes’ Accessibility Assessment Using Geographic Information System Processing and Deep Learning-Based Object Detection
by Tomás E. Martínez-Chao, Agustín Menéndez-Díaz, Silverio García-Cortés and Pierpaolo D’Agostino
Sensors 2024, 24(11), 3667; https://doi.org/10.3390/s24113667 - 5 Jun 2024
Viewed by 2166
Abstract
The need to establish safe, accessible, and inclusive pedestrian routes is considered one of the European Union’s main priorities. We have developed a method of assessing pedestrian mobility in the surroundings of urban public buildings to evaluate the level of accessibility and inclusion, [...] Read more.
The need to establish safe, accessible, and inclusive pedestrian routes is considered one of the European Union’s main priorities. We have developed a method of assessing pedestrian mobility in the surroundings of urban public buildings to evaluate the level of accessibility and inclusion, especially for people with reduced mobility. In the first stage of assessment, artificial intelligence algorithms were used to identify pedestrian crossings and the precise geographical location was determined by deep learning-based object detection with satellite or aerial orthoimagery. In the second stage, Geographic Information System techniques were used to create network models. This approach enabled the verification of the level of accessibility for wheelchair users in the selected study area and the identification of the most suitable route for wheelchair transit between two points of interest. The data obtained were verified using inertial sensors to corroborate the horizontal continuity of the routes. The study findings are of direct benefit to the users of these routes and are also valuable for the entities responsible for ensuring and maintaining the accessibility of pedestrian routes. Full article
(This article belongs to the Special Issue Emerging Remote Sensing Techniques and Applications for Object Detection)
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27 pages, 17784 KiB  
Article
Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning
by Rong Hou, Jianping Yin, Yanchen Liu and Huijuan Lu
Sensors 2024, 24(3), 984; https://doi.org/10.3390/s24030984 - 2 Feb 2024
Cited by 2 | Viewed by 1746
Abstract
In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult [...] Read more.
In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs. Full article
(This article belongs to the Special Issue Emerging Remote Sensing Techniques and Applications for Object Detection)
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