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Deep embedded multi-view Object clustering using Aerial images in the wild
Conference proceeding   Peer reviewed

Deep embedded multi-view Object clustering using Aerial images in the wild

Don Yates, Hakki Erhan Sevil, Arash Mahyari and David Gray
Proc. SPIE 13463, Automatic Target Recognition XXXV, 134630H, Vol.Proceedings Volume 13463
SPIE Defense + Commercial Sensing (Orlando, Florida, USA, 04/12/2025–04/16/2025)
05/29/2025
DOI: https://doi.org/10.1117/12.3053614

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Abstract

Multiview clustering has garnered attention, with many methods showing success on simple datasets such as MNIST. However, many state-of-the-art methods lack sufficient representational capability. This paper presents results of deep embedded clustering on multiview real-world aerial imaging data. Adapting previous methods to the challenges of aerial imagery, the approach introduces a ResNet-18 autoencoder backbone and data augmentation techniques to handle complex images and diverse environmental conditions. Advanced feature extraction using convolutional autoencoders captures intricate patterns and spatial relationships. By integrating multiview data, the self-supervised method enhances clustering accuracy and robustness, advancing aerial image analysis for environmental monitoring, urban planning, and first responder efforts.

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