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Special Issue "Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 30 December 2023 | Viewed by 2953

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Special Issue Editors

Dr. Guangliang Cheng
E-Mail Website
Guest Editor

Department of Computer Science, University of Liverpool, Liverpool, UK
Interests: computer vision; remote sensing; change detection; hyperspectral image classification; road extraction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qi Zhao

E-Mail Website
Guest Editor

School of Electronic and Information Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
Interests: remote sensing; image recognition; domain adaptation; few-shot learning; light-weight neural network
Special Issues, Collections and Topics in MDPI journals

Dr. Paolo Tripicchio

E-Mail Website
Guest Editor

Gustavo Stefanini Advanced Robotics Research Center, Scuola Superiore Sant’Anna di Studi Universitari e di Perfezionamento, Pisa, Italy
Interests: artificial intelligence; computer vision; 3D reconstruction; image processing; localization methods; mapping; inspection robotics; deep Learning; industrial monitoring; smart sensors; photogrammetry; LiDAR; SAR; farming applications
Special Issues, Collections and Topics in MDPI journals

Dr. Hossein M. Rizeei

E-Mail Website
Guest Editor

1. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
2. McGregor Coxall Australia Pty Ltd., Sydney, NSW, Australia
Interests: machine learning; geospatial 3D analysis; geospatial database querying; web GIS; airborne/spaceborne image processing; feature extraction; time-series analysis in forecasting modelling and domain adaptation in various environmental applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue on “Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications” aims to explore the intersection of artificial intelligence (AI) and remote sensing, showcasing cutting-edge methodologies and their diverse applications in this field. Remote sensing, the science of collecting information about the earth's surface from a distance, has become an invaluable tool for monitoring and understanding our planet’s dynamics and changes. Concurrently, artificial intelligence techniques have advanced significantly, offering powerful tools for data analysis and decision making. The integration of AI techniques with remote sensing data has revolutionized the way we analyze, interpret, and extract meaningful information from large-scale Earth observation datasets, enabling us to address critical environmental, social, and economic challenges.

This Special Issue provides a platform for researchers and practitioners to present their latest research findings, methodologies, and applications in the realm of AI for remote sensing. It serves as a forum to exchange knowledge and foster collaborations among experts from diverse disciplines, including computer science, remote sensing, geoscience, and environmental studies. The issue aims to advance the understanding and utilization of AI techniques for remote sensing applications, pushing the boundaries of what can be achieved in terms of data analysis, information extraction, and decision support.

This Special Issue emphasizes the latest advancements in AI algorithms, models, and techniques that have been specifically developed or adapted for remote sensing applications. It aims to showcase novel methodologies, innovative applications, and case studies that demonstrate the potential of AI in addressing real-world challenges in agriculture, urban planning, forestry, climate change, and other domains.

We invite submissions that address various aspects of AI methodologies and their applications in remote sensing. Potential topics of interest include, but are not limited to:

AI-driven image classification and recognition in remote sensing.
Deep learning techniques for feature extraction and representation learning from remote sensing data.
The fusion of multi-source remote sensing data using AI-based approaches.
Semantic segmentation and object detection in remote sensing images
AI-based approaches for change detection and monitoring using remote sensing data.
AI-enabled hyperspectral and LiDAR data analysis.
Transfer learning and domain adaptation for remote sensing applications.
Few-shot image recognition/semantic segmentation/object detection, etc.
Domain adaptation problems in the remote sensing area.
Case studies and applications of AI in remote sensing for agriculture, urban planning, forestry, climate change, etc.

Authors are invited to submit original research articles, reviews, or survey papers that contribute to the field of advanced AI for remote sensing. All submissions will undergo a rigorous peer review process to ensure the quality and relevance of accepted papers. Manuscripts should follow the guidelines provided by the journal and should clearly address the Special Issue theme

Dr. Guangliang Cheng
Prof. Dr. Qi Zhao
Dr. Paolo Tripicchio
Dr. Hossein M. Rizeei
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

artificial intelligence
remote sensing
image recognition
semantic segmentation
object detection
change detection
environmental monitoring
deep learning
few-shot learning
domain adaptation

Published Papers (3 papers)

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Research

23 pages, 5562 KiB

Open AccessArticle

SMFF-YOLO: A Scale-Adaptive YOLO Algorithm with Multi-Level Feature Fusion for Object Detection in UAV Scenes

and

Remote Sens. 2023, 15(18), 4580; https://doi.org/10.3390/rs15184580 - 18 Sep 2023

Viewed by 1022

Abstract

Object detection in images captured by unmanned aerial vehicles (UAVs) holds great potential in various domains, including civilian applications, urban planning, and disaster response. However, it faces several challenges, such as multi-scale variations, dense scenes, complex backgrounds, and tiny-sized objects. In this paper, we present a novel scale-adaptive YOLO framework called SMFF-YOLO, which addresses these challenges through a multi-level feature fusion approach. To improve the detection accuracy of small objects, our framework incorporates the ELAN-SW object detection prediction head. This newly designed head effectively utilizes both global contextual information and local features, enhancing the detection accuracy of tiny objects. Additionally, the proposed bidirectional feature fusion pyramid (BFFP) module tackles the issue of scale variations in object sizes by aggregating multi-scale features. To handle complex backgrounds, we introduce the adaptive atrous spatial pyramid pooling (AASPP) module, which enables adaptive feature fusion and alleviates the negative impact of cluttered scenes. Moreover, we adopt the Wise-IoU(WIoU) bounding box regression loss to enhance the competitiveness of different quality anchor boxes, which offers the framework a more informed gradient allocation strategy. We validate the effectiveness of SMFF-YOLO using the VisDrone and UAVDT datasets. Experimental results demonstrate that our model achieves higher detection accuracy, with AP50 reaching 54.3% for VisDrone and 42.4% for UAVDT datasets. Visual comparative experiments with other YOLO-based methods further illustrate the robustness and adaptability of our approach. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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20 pages, 3522 KiB

Open AccessArticle

Detection Method of Infected Wood on Digital Orthophoto Map–Digital Surface Model Fusion Network

and

Remote Sens. 2023, 15(17), 4295; https://doi.org/10.3390/rs15174295 - 31 Aug 2023

Viewed by 481

Abstract

Pine wilt disease (PWD) is a worldwide affliction that poses a significant menace to forest ecosystems. The swift and precise identification of pine trees under infection holds paramount significance in the proficient administration of this ailment. The progression of remote sensing and deep learning methodologies has propelled the utilization of target detection and recognition techniques reliant on remote sensing imagery, emerging as the prevailing strategy for pinpointing affected trees. Although the existing object detection algorithms have achieved remarkable success, virtually all methods solely rely on a Digital Orthophoto Map (DOM), which is not suitable for diseased trees detection, leading to a large false detection rate in the detection of easily confused targets, such as bare land, houses, brown herbs and so on. In order to improve the ability of detecting diseased trees and preventing the spread of the epidemic, we construct a large-scale PWD detection dataset with both DOM and Digital Surface Model (DSM) images and propose a novel detection framework, DDNet, which makes full use of the spectral features and geomorphological spatial features of remote sensing targets. The experimental results show that the proposed joint network achieves an AP50 2.4% higher than the traditional deep learning network. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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20 pages, 7667 KiB

Open AccessArticle

TPH-YOLOv5-Air: Airport Confusing Object Detection via Adaptively Spatial Feature Fusion

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Remote Sens. 2023, 15(15), 3883; https://doi.org/10.3390/rs15153883 - 05 Aug 2023

Cited by 1 | Viewed by 812

Abstract

Airport detection in remote sensing scenes is a crucial area of research, playing a key role in aircraft blind landing procedures. However, airport detection in remote sensing scenes still faces challenges such as class confusion, poor detection performance on multi-scale objects, and limited dataset availability. To address these issues, this paper proposes a novel airport detection network (TPH-YOLOv5-Air) based on adaptive spatial feature fusion (ASFF). Firstly, we construct an Airport Confusing Object Dataset (ACD) specifically tailored for remote sensing scenarios containing 9501 instances of airport confusion objects. Secondly, building upon the foundation of TPH-YOLOv5++, we adopt the ASFF structure, which not only enhances the feature extraction efficiency but also enriches feature representation. Moreover, an adaptive spatial feature fusion (ASFF) strategy based on adaptive parameter adjustment module (APAM) is proposed, which improves the feature scale invariance and enhances the detection of airports. Finally, experimental results based on the ACD dataset demonstrate that TPH-YOLOv5-Air achieves a mean average precision (mAP) of 49.4%, outperforming TPH-YOLOv5++ by 2% and the original YOLOv5 network by 3.6%. This study contributes to the advancement of airport detection in remote sensing scenes and demonstrates the practical application potential of TPH-YOLOv5-Air in this domain. Visualization and analysis further validate the effectiveness and interpretability of TPH-YOLOv5-Air. The ACD dataset is publicly available. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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Special Issue "Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications"

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