A Novel Temporal Dynamic Multi-Scale Spiking Transformer for Aerial Scene Classification

DOI

10.2991/978-94-6239-616-6_40How to use a DOI?

Keywords

Aerial scene Classification; Spiking Network; Feature extraction; Deep-Learning; Perfumer Optimization; Remote Sensing; Spiking Transformer; Neuromorphic Computation

Abstract

Aerial scene understanding is important for applications involving land use analysis, environmental monitoring and urban planning based on high-resolution drone and satellite imagery. Existing methodologies are limited due to the temporal variation, noise sensitivity, and the limited capability of regional generalization. To overcome these challenges, an EfficientNet-enhanced Temporal Dynamic Multi-scale Spiking Transformer (TDMST) framework is proposed to facilitate adaptive and energy-efficient classification of aerial scenes. EfficientNetV2 extracts low- and high-level features. The TDMST framework further processes the extracted features using multi-branch spiking neural mechanisms, along with transformer-like global attention mechanisms. Hyperparameters and architectural features are tuned using the Perfumer Optimization Algorithm (POA). Experiments conducted on the WHU-RS19, RSSCN7, and AID datasets achieved accuracies of 99.7%, 99.6%, and 99.3% respectively, demonstrating the TDMST’s ability to securely and reliably classify aerial scenes, making it incredibly suitable for remote sensing applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Shunmugapriya
AU  - M. Thachayani
PY  - 2026
DA  - 2026/03/31
TI  - A Novel Temporal Dynamic Multi-Scale Spiking Transformer for Aerial Scene Classification
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 531
EP  - 541
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_40
DO  - 10.2991/978-94-6239-616-6_40
ID  - Shunmugapriya2026
ER  -