Semantic Segmentation of Aerial Imagery: A Novel Approach Leveraging Hierarchical Multi-Scale Features and Channel-Based Attention for Drone Applications

Abstract

Drone semantic segmentation is a challenging task in computer vision, mainly due to the inherent complexities associated with aerial imagery. This paper presents a comprehensive methodology for drone semantic segmentation and evaluates its performance using the ICG dataset. The proposed method leverages hierarchical multi-scale feature extraction and efficient channel-based attention Atrous Spatial Pyramid Pooling (ASPP) to address the unique challenges encountered in this domain. In this study, the performance of the proposed method is compared to several state-of-the-art models. The findings of this research highlight the effectiveness of the proposed method in tackling the challenges of drone semantic segmentation. The outcomes demonstrate its superiority over the state-of-the-art models, showcasing its potential for accurate and efficient segmentation of aerial imagery. The results contribute to the advancement of drone-based applications, such as surveillance, object tracking, and environmental monitoring, where precise semantic segmentation is crucial. The obtained experimental results demonstrate that the proposed method outperforms these existing approaches regarding Dice, mIOU, and accuracy metrics. Specifically, the proposed method achieves an impressive performance with Dice, mIOU, and accuracy scores of 86.51%, 76.23%, and 91.74%, respectively.