Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Article number880
JournalAerospace
Volume10
Issue number10
DOIs
StatePublished - Oct 2023

Keywords

  • combined segmentation network
  • dataset
  • semantic drone dataset (SDD)
  • semantic segmentation
  • UAVid
  • unmanned aerial vehicle (UAV)

Fingerprint

Dive into the research topics of 'Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets'. Together they form a unique fingerprint.

Cite this