Aerial image feature mapping using deep neural networks

S. Parisa Dajkhosh; Orges Furxhi; C. Kyle Renshaw; Eddie L. Jacobs

doi:10.1117/12.3014121

27 August 2024 Aerial image feature mapping using deep neural networks

S. Parisa Dajkhosh, Orges Furxhi, C. Kyle Renshaw, Eddie L. Jacobs

Author Affiliations +

Proceedings Volume PC13037, Geospatial Informatics XIV; PC1303701 (2024) https://doi.org/10.1117/12.3014121
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

This research focuses on the analysis of aerial imagery, specifically satellite and terrain images sourced from the Google Maps API. The primary objective is to develop a deep learning framework capable of discerning images taken from the same geographical location, leveraging common features present in both satellite and terrain imagery. This endeavor involves the utilization of transfer learning in a Siamese network to extract meaningful feature maps. By identifying feature maps that represent shared attributes, it becomes plausible to establish connections between these typically disparate data sources. This linkage, in turn, augments the precision of geospatial analysis. This research not only promises advancements in geospatial data analysis but also extends its impact to broader domains, including remote sensing, environmental science, and urban planning by enabling the harmonious integration of diverse aerial data sources.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

S. Parisa Dajkhosh, Orges Furxhi, C. Kyle Renshaw, and Eddie L. Jacobs "Aerial image feature mapping using deep neural networks", Proc. SPIE PC13037, Geospatial Informatics XIV, PC1303701 (27 August 2024); https://doi.org/10.1117/12.3014121

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