Object Detection in Remote Sensing Images Based on Artificial Intelligence
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 29 December 2025 | Viewed by 1263
Special Issue Editors
Interests: optical remote sensing image; automatic target detection and recognition; multi-source feature fusion; image interpretation application; artificial intelligence
Interests: optical remote sensing image; automatic target detection and recognition; new system imaging; image acquisition and processing
Interests: optical remote sensing image; multimodal remote sensing; data fusion; foundation models; object detection; semantic segmentation
Interests: hyperspectral image processing; deep learning; Image interpretation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of remote sensing technologies, including high-resolution satellites, unmanned aerial vehicles (UAVs), and aerial sensors, has generated an unprecedented volume of geospatial data. Extracting meaningful information from this vast resource is critical for applications such as environmental monitoring, urban planning, disaster management, and agriculture. Object detection in remote sensing images (RSIs) plays a pivotal role in automating the identification and localization of objects (e.g., vehicles, buildings, ships, aircraft) within complex, large-scale scenes. However, the unique challenges of RSIs—such as varying scales, arbitrary orientations, dense arrangements, occlusions, and diverse background clutter—significantly hinder the performance of traditional computer vision methods.
Recent breakthroughs in artificial intelligence (AI), particularly deep learning (DL), have revolutionized object detection in RSIs. Techniques like convolutional neural networks (CNNs), transformer-based architectures, and hybrid models have demonstrated remarkable capabilities in addressing domain-specific challenges, enabling higher accuracy, robustness, and efficiency. Despite these advances, critical gaps remain, including the need for lightweight models for edge deployment, generalization across heterogeneous datasets, interpretability of AI decisions, and handling of low-resolution or weakly annotated data. Furthermore, emerging trends such as multimodal data fusion and few-shot learning demand deeper exploration.
This Special Issue seeks to compile cutting-edge research on AI-driven object detection in RSIs, emphasizing novel algorithms, benchmark datasets, and real-world applications. By fostering interdisciplinary collaboration, we aim to accelerate progress in this field, bridging the gap between theoretical innovation and practical implementation to meet the growing demands of global remote sensing communities.
(1) Advanced deep learning architectures for RSI object detection.
(2) Robust target detection methods under complex conditions such as dense target arrangement, occlusion, and background interference.
(3) Multi-modal data fusion detection models, such as optical, LiDAR, SAR, hyperspectral, multispectral and infrared.
(4) Weakly supervised, semi-supervised, or unsupervised object detection frameworks for scenarios with scarce annotations.
(5) Lightweight detection models for satellites, UAVs (Unmanned Aerial Vehicles), and other edge devices.
(6) New datasets and benchmarks for specific detection tasks.
Dr. Jianming Hu
Dr. Xiyang Zhi
Dr. Yong-Qiang Mao
Dr. Longfei Ren
Guest Editors
Manuscript Submission Information
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Keywords
- remote sensing images
- object detection
- artificial intelligence
- multimodal data fusion
- frontier applications
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