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Remote Sensing
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Advanced AI Technology for Remote Sensing Analysis
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Advanced AI Technology for Remote Sensing Analysis

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1420

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. Shuchang Lyu

E-Mail Website
Guest Editor
School of Electronic Information Engineering, Beihang University, Beijing, China
Interests: compact CNN design; remote sensing scene image recognition; transfer learning and domain adaptation
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 rapid evolution of artificial intelligence (AI) has precipitated remarkable advancements across various scientific and engineering domains. Among these, remote sensing stands out as a critical area where AI technologies, including machine learning and deep learning, are increasingly integrated to revolutionize how we acquire, process, and interpret Earth observation data. Despite the inspiring success of previous work based on standard benchmark datasets, new advanced AI technologies still need to be further clarified and real-world downstream tasks still need to be further explored. This Special Issue aims to explore the cutting-edge intersections of AI technology and remote sensing analysis, presenting novel research findings, methodological developments, and comprehensive reviews.

This Special Issue aims to investigate the application of advanced AI technologies for remote sensing analysis. We invite submissions that address various aspects of remote sensing analysis and the corresponding advanced AI technologies in remote sensing scenes. Potential topics of interest include, but are not limited to, the following:

  • Multi-modality (multi-source) image fusion;
  • Cross-domain adaptation or multi-domain generalization methods;
  • Multi-modality remote sensing image benchmark datasets;
  • Multi-modality large foundation models for remote sensing scenes;
  • Visual–linguistic models for remote sensing scenes;
  • Case studies and applications of multi-modality networks in remote sensing for agriculture, urban planning, forestry, climate change, etc.;
  • Multi-view joint analysis of remote sensing scenes;
  • Image segmentation/detection or change detection;
  • Remote sensing analysis with foundation models.

Authors are invited to submit original research articles  (including reviews, articles, technical notes, and communications) 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. Shuchang Lyu
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

  • multi-modality images
  • image fusion
  • cross-domain adaptation
  • visual–linguistic models
  • multi-modality downstream tasks
  • remote sensing
  • pattern analysis
  • advanced AI technology
  • image segmentation/detection and change detection

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Related Special Issue

Published Papers (2 papers)

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Research

19 pages, 1243 KiB  
Article
Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets
by Xu Gu, Xi Yang, Hong Liu and Dong Yang
Remote Sens. 2024, 16(22), 4138; https://doi.org/10.3390/rs16224138 - 6 Nov 2024
Viewed by 483
Abstract
Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. [...] Read more.
Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. Conventional pose estimation for space targets unfolds in two stages: establishing 2D–3D correspondences using keypoint detection networks and 3D models, followed by pose estimation via the perspective-n-point algorithm. The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. To tackle the aforementioned challenges, we propose an adaptive dual-stream aggregation network (ADSAN), which enables the learning of finer local representations and the acquisition of abundant spatial and semantic information by merging features from both inter-layer and intra-layer perspectives through a multi-grained approach, consolidating features within individual layers and amplifying the interaction of distinct resolution features between layers. Furthermore, our ADSAN implements the selective keypoint focus module (SKFM) algorithm to alleviate problems caused by partial occlusions and viewpoint alterations. This mechanism places greater emphasis on the most challenging keypoints, ensuring the network prioritizes and optimizes its learning around these critical points. Benefiting from the finer and more robust information of space objects extracted by the ADSAN and SKFM, our method surpasses the SOTA method PoET (5.8°, 8.1°/0.0351%, 0.0744%) by 0.5°, 0.9°, and 0.0084%, 0.0354%, achieving 5.3°, 7.2° in rotation angle errors and 0.0267%, 0.0390% in normalized translation errors on the Speed and SwissCube datasets, respectively. Full article
(This article belongs to the Special Issue Advanced AI Technology for Remote Sensing Analysis)
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22 pages, 5938 KiB  
Article
MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation
by Yanle Pei, Qian Li, Yayi Wu, Xuan Peng, Shiqing Guo, Chengzhi Ye and Tianying Wang
Remote Sens. 2024, 16(19), 3597; https://doi.org/10.3390/rs16193597 - 26 Sep 2024
Viewed by 658
Abstract
Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of [...] Read more.
Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset. Full article
(This article belongs to the Special Issue Advanced AI Technology for Remote Sensing Analysis)
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