Deep Learning Algorithms for Remote Sensing
| dc.contributor.author | Bouketir Hadda | |
| dc.contributor.author | Habbeche Dounia | |
| dc.date.accessioned | 2025-05-25T10:08:33Z | |
| dc.date.issued | 2024 | |
| dc.description | Unmanned Aerial Vehicles (UAVs) have revolutionized remote sensing, offering continuous access to high-resolution data from previously inaccessible areas. This data is particularly valuable for semantic segmentation, a deep learning technique crucial for tasks like land cover mapping and object detection in environmental monitoring and urban planning. However, effectively analyzing this data can be hampered by the vanishing gradient problem, where information gets progressively lost during training in deep neural networks. This research addresses this challenge by leveraging deep learning and specifically tackling the vanishing gradient problem. We integrate a U-Net architecture with a ResNet34 backbone. The ResNet architecture is a deep learning model specifically designed to mitigate the vanishing gradient problem. This allows the model to learn complex features within UAV imagery for semantic segmentation tasks with greater depth and effectiveness. We trained and evaluated the model on diverse UAV datasets (LandCover.ai, Aerial Semantic Segmentation, UAVid, AeroScapes) leveraging GPUs, the Adam optimizer, and the Dice loss function. We meticulously optimized hyperparameters and implemented specific training strategies to maximize performance. Our approach achieves remarkable results, with Intersection over Union (IoU) scores exceeding 90% on LandCover.ai and AeroScapes datasets (92.0% and 91.0%, respectively). Compared to existing methods (DeepLabv3, FCN-8, RCCT-ASPPNet, SS-Inhi-VGG16-FFT), our model demonstrates superior accuracy across all metrics (precision, recall, F1-score, mIoU). These findings solidify the power of deep learning for accurate and efficient large-scale geospatial analysis in remote sensing. Looking ahead, this research paves the way for further exploration of deep learning and UAV data integration for semantic segmentation tasks. Future work can refine the proposed approach, investigate new applications, and validate its robustness in real-world scenarios | |
| dc.description.abstract | Deep learning has revolutionized the analysis of data collected from unmanned aerial vehicle (UAV) imagery, allowing for more profound insights, precise analysis, and enhanced data extraction. This advancement has significantly contributed to the refinement of semantic segmentation techniques. Particularly convolutional neural networks (CNNs) have emerged as powerful tools in this domain, outperforming traditional methodologies. Nonetheless, challenges persist, including feature extraction, class imbalance issues, overfitting, and vanishing gradients that hinder deep neural network training, consequently impacting segmentation performance. To address these challenges, we propose a novel approach by integrating the U-Net architecture with ResNet34 backbone leveraging its strong feature extraction capabilities. These features are further improved by using trained weights from the ImageNet dataset. We train and evaluate the proposed model on several UAV datasets, including Aerial Semantic Segmentation, LandCover.ai, UAVid, and AeroScapes. We achieve remarkable performance, higher accuracy, precision, recall, F1-score, and miou compared to other methods. | |
| dc.identifier.uri | https://dspace.univ-bba.dz/handle/123456789/273 | |
| dc.language.iso | fr | |
| dc.publisher | university of bordj bou arreridj | |
| dc.relation.ispartofseries | MM/857 | |
| dc.subject | : Remote Sensing | |
| dc.subject | UAV | |
| dc.subject | Semantic Segmentation | |
| dc.subject | Deep Learning | |
| dc.subject | U-Net | |
| dc.subject | ResNet34 | |
| dc.title | Deep Learning Algorithms for Remote Sensing | |
| dc.type | Thesis |