Affiliation:
1. Department of Location-Based Information System, Kyungpook National University, Gyeongsang-daero, Gyeongsangbuk-do, Sangju 2559, Republic of Korea
Abstract
Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. However, these techniques heavily rely on the size of the training data, and obtaining large RS imagery datasets is difficult (compared to RGB images), primarily due to environmental factors such as atmospheric conditions and relief displacement. Unmanned aerial vehicle (UAV) imagery presents unique challenges, such as variations in object appearance due to UAV flight altitude and shadows in urban areas. This study analyzed the combined segmentation network (CSN) designed to train heterogeneous UAV datasets effectively for their segmentation performance across different data types. Results confirmed that CSN yielded high segmentation accuracy on specific classes and can be used on diverse data sources for UAV image segmentation. The main contributions of this study include analyzing the impact of CSN on segmentation accuracy, experimenting with structures with shared encoding layers to enhance segmentation accuracy, and investigating the influence of data types on segmentation accuracy.
Funder
National Research Foundation of Korea NRF
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献