TY - JOUR
T1 - Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets
AU - Song, Ahram
N1 - Publisher Copyright:
© 2023 by the author.
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - combined segmentation network
KW - dataset
KW - semantic drone dataset (SDD)
KW - semantic segmentation
KW - UAVid
KW - unmanned aerial vehicle (UAV)
UR - http://www.scopus.com/inward/record.url?scp=85175160316&partnerID=8YFLogxK
U2 - 10.3390/aerospace10100880
DO - 10.3390/aerospace10100880
M3 - Article
AN - SCOPUS:85175160316
SN - 2226-4310
VL - 10
JO - Aerospace
JF - Aerospace
IS - 10
M1 - 880
ER -