Browse Journals & Magazines >IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
UAV Remote Sensing Monitoring and Applications
Volume 18: 2025
Showing 1-10 of 10
UAV Remote Sensing Monitoring and Applications
Sort By
Lightweight Dual-Stream SAR–ATR Framework Based on an Attention Mechanism-Guided Heterogeneous Graph Network
Xuying Xiong;Xinyu Zhang;Weidong Jiang;Tianpeng Liu;Yongxiang Liu;Li Liu
Publication Year: 2025,Page(s):537 - 556
Cited by: Papers (1)
Current methods synthetic aperture radar–automatic target recognition (SAR–ATR) research methods still struggle with overfitting due to small amounts of training data, as well as black-box opacity and high computational requirements. Unmanned aerial vehicles, as the mainstream means of acquiring SAR data, place higher requirements on ATR algorithms due to their flexible maneuvering characteristics...Show More
Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models
Zhongwen Hu;Jinhua Zhang;Zhigang Liu;Yinghui Zhang;Jingzhe Wang;Qian Zhang;Guofeng Wu
Publication Year: 2025,Page(s):757 - 768
The photogrammetric 3-D textured mesh model (TMM) obtained by unmanned aerial vehicle provides accurate geometric shapes and realistic textures. The 3-D semantics derived from TMMs serve as the foundation in many applications, such as urban planning, forestry, and smart city. Although 3-D TMM provides more features than 2-D images, current classification methods have not fully utilized these featu...Show More
Lightweight Spatial–Spectral Shift Module With Multihead MambaOut for Hyperspectral Image Classification
Publication Year: 2025,Page(s):921 - 934
In hyperspectral images, the high dimensionality of spectral data often leads to redundant spectral information, making it difficult to extract features. Two-dimensional CNNs fail to effec-tively extract spatial and spectral information, and deploying three-dimensional CNNs on microprocessors is challenging as these net-works consume excessive resources. If graph convolutional networks (GCN) are a...Show More
A Block Registration Strategy for Unmanned Aerial Vehicle Orthorectified Images With Whole First and Local Later
Publication Year: 2025,Page(s):1670 - 1680
In response to the registration error problem caused by different degrees of local distortion in different positions of drone orthophoto images during two periods, this article proposes a block-based registration strategy suitable for drone orthophoto images, which is first global and then local. This strategy first uses the Fourier Merlin transform to perform overall registration on the drone ort...Show More
CAIL: Cross-Modal Vehicle Reidentification in Aerial Images Using the Centroid-Aligned Implicit Learning Network
Haoran Gao;Yiming Yan;Yanming He;
Publication Year: 2025,Page(s):2577 - 2588
With the rapid development of autonomous aerial vehicles (AAV) remote sensing equipment, multimodal image data in the remote sensing field have exploded in recent years.In order to effectively alleviate the differences between the three modalities of SAR, visible light, and infrared, we proposed a vehicle reidentification (Re-ID) task based on multimodal aerial images. Compared with traditional Re...Show More
A Dynamic Cascade Cross-Modal Coassisted Network for AAV Image Object Detection
Shu Tian;Li Wang;Lin Cao;Lihong Kang;Xian Sun;Jing Tian;Xiangwei Xing;Bo Shen;Chunzhuo Fan;Kangning Du;Chong Fu;Ye Zhang
Publication Year: 2025,Page(s):2749 - 2765
Accurate detection of small objects plays an important role in the application of Autonomous aerial vehicles (AAV). However, current works mainly extract comprehensive features from unimodal images, which can obtain very limited distinguishable features for objects, especially those with small sizes. To address this issue, we propose a dynamic cascade cross-modal coassisted network, which integrat...Show More
Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation
Xiaoyu Xu;Weida Zhan;Yichun Jiang;Depeng Zhu;Yu Chen;Jinxin Guo;Ziqiang Hao;Deng Han
Publication Year: 2025,Page(s):2829 - 2850
The imbalance between positive and negative samples and the loss of small targets in complex backgrounds are catastrophic for infrared small target detection. To address these issues, we proposed an infrared small target detection method based on weak feature enhancement and target adaptive proliferation (IRSTD-WFETAP). First, we utilized a sparse sampling mechanism and hybrid filtering method to ...Show More
UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review
Zhen Zhang;Lehao Huang;Qingwang Wang;Linhuan Jiang;Yemao Qi;Shunyuan Wang;Tao Shen;Bo-Hui Tang;Yanfeng Gu
Publication Year: 2025,Page(s):3099 - 3124
In recent years, significant advances in unmanned aerial vehicle (UAV) technology and hyperspectral remote sensing have spurred rapid and innovative developments in UAV-based hyperspectral image (HSI) classification across a range of fields, including environmental monitoring, precision agriculture, forest health assessment, and disaster management. Compared to spaceborne platforms, the spectra of...Show More
Wavelet-Transform-Based Neural Network for Tidal Flat Remote Sensing Image Deblurring
Publication Year: 2025,Page(s):6152 - 6163
In response to the challenge of image degradation caused by strong sea breezes during drone surveillance over tidal flats, conventional methodologies have predominantly employed iterative upsampling and downsampling techniques to augment the receptive field of the network. However, this approach is prone to the loss of critical texture data within the tidal flat imagery throughout the sampling pro...Show More
Early Forest Fire Detection With UAV Image Fusion: A Novel Deep Learning Method Using Visible and Infrared Sensors
Kunlong Niu;Chongyang Wang;Jianhui Xu;Jianrong Liang;Xia Zhou;Kaixiang Wen;Minjian Lu;Chuanxun Yang
Publication Year: 2025,Page(s):6617 - 6629
Global warming has significantly increased the frequency of forest fires. Unmanned aerial vehicles (UAVs) provide rapid response and real-time monitoring, offering unique advantages over traditional human inspections and satellite monitoring. Their ability to monitor large forest areas during the early stages of fires supports timely warning. UAVs typically detect fires by capturing visible and in...Show More
Contact Information
Contact Information
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.