On optimizing morphological neural networks for hyperspectral image classification

Maksim Kukushkin; Martin Bogdan; Thomas Schmid

doi:10.1117/12.3023593

3 April 2024 On optimizing morphological neural networks for hyperspectral image classification

Maksim Kukushkin, Martin Bogdan, Thomas Schmid

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 1307202 (2024) https://doi.org/10.1117/12.3023593
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

Abstract

Convolutional Neural Networks have become an important tool for various Computer Vision tasks. Yet, increasing complexity of such architectures drives computational costs. To this end, we propose two measures to achieve similar classification results as state-of-the-art architectures while at the same time reducing model complexity significantly. Firstly, we describe a novel type of non-linear parameter-efficient morphological layers inspired by concepts that are well-known and widely used with convolutions. Secondly, we present a set of simple network architectures, organized as optimization framework, which is enhanced by neural architecture search and hyperparameter optimization. In experiments with hyperspectral remote sensing data, we demonstrate that the identified optimal morphological architecture produces results not only comparable with other architectures from the optimization framework, but also comparable or better than selected state-of-the-art neural network architectures for image classification. Depending on the performed task, the proposed optimized architecture requires up to 25 times fewer parameters than actual state-of-the-art networks.

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

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Maksim Kukushkin, Martin Bogdan, and Thomas Schmid "On optimizing morphological neural networks for hyperspectral image classification", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 1307202 (3 April 2024); https://doi.org/10.1117/12.3023593

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KEYWORDS

Convolution

Education and training

Data modeling

Image classification

Neural networks

Hyperspectral imaging

Mathematical optimization

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