Aug 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 262-272· 0 citations· 18 references
TL;DR
A hybrid deep learning method, which combines Convolutional Neural Networks, STDNet model and CFAR-based detection, with the objective to enhance performance of multi-scale object detection, is proposed.
Abstract
Synthetic Aperture Radar (SAR) images always provide high-resolution data in all weather and lighting circum-stances. However, speckle noise, clutter backgrounds and scale variation remain as significant challenges for accurate target detection on SAR images. This paper proposes a hybrid deep learning method, which combines Convolutional Neural Networks (CNN), STDNet model and CFAR-based detection, with the objective to enhance performance of multi-scale object detection. The outputs from both the CNN and STDNet branches are fused using an Intersection over Union (IoU)-based fusion strategy helps to achieve better detection accuracy. The experimental results show that the proposed hybrid model reaches better precision, recall, and F1-score than separate classifiers. This proposed approach is a cost-effective and practical strategy for tracking different real-world SAR targets.
Synthetic aperture radar (SAR) change detection is important for real-world monitoring because it can operate under all-weather and day–night conditions, but accurate detection remains challenging due to speckle noise and complex scene variations. This paper presents a Siamese Mamba network for SAR change detection, de...
Mohamed Ihmeida, T. Saleh· International Conference on...· 0 citations
Automatic classification of military aircraft in satellite imagery is a challenging problem with a high risk of error due to the limited pixel area of targets, variations in image resolution and illumination conditions, background complexity, and strong visual similarity among classes. In this study, a deep learning ap...
The automatic recognition of objects in aerial thermal images plays a very crucial role in many real-time applications. The advancement of Unmanned Aerial Vehicles (UAVs) has enabled real-time surveillance and monitoring in sectors such as defense, agriculture, and disaster management. Small target size, limited spatia...
B. Ashwini, G. Muthupandi· Engineering, Technology &...· 0 citations
Oriented object detection in remote sensing images plays an important role in maritime monitoring, airport surveillance, and traffic management. However, densely distributed small objects and slender-structured objects remain highly challenging to detect because they are susceptible to object adhesion, background inter...
Ya-Ting Guo, Jin-Fu Yang, Fang-Xuan Fan et al.· IEEE Geoscience and Remote S...· 0 citations
This paper proposes a novel Transformer-based neural network architecture specifically designed for radar signal processing that integrates multi-head self-attention mechanisms with temporal convolutional networks to effectively model both local patterns and global dependencies in radar data.
Jun Hu, Cheng Yu, Yahui Hu et al.· International Conference on...· 0 citations
A novel model is introduced by assessing the impacts of several YOLO object detection algorithms with the Convolutional Block Attention Module (CBAM) on aircraft detection from satellite images to demonstrate that attention mechanisms have a significant impact when used with the YOLO architecture for object detection i...
Ibrahim Aruk, Hakan Açıkgöz, Ertuğrul Doğruluk· Konya Journal of Engineering...· 0 citations
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