ML Anthology

Accounting for Spatial Variability with the Histogram of Oriented Gradients Based Masking Improves Performance of Masked Autoencoder over Hyperspectral Satellite Imagery (Student Abstract)

Tanjim Bin Faruk, Abdul Matin, Shrideep Pallickara, Sangmi Lee Pallickara
AAAI 2025 pp. 29365-29367
doi:10.1609/AAAI.V39I28.35253 /aaai/2025/faruk2025aaai-accounting/

Abstract

Masked autoencoders employ random masking to effectively reconstruct input images using self-supervised techniques, which allows for efficient training on large datasets. However, the random masking strategy does not adequately tap into information encapsulated within high-dimensional hyperspectral satellite imagery that is used in several domains. We propose a novel masking strategy, HOGMAE, based on the Histogram of Oriented Gradients that incorporates rich information inherent within satellite images during the mask creation step. Our experiments, over a hyperspectral satellite dataset, demonstrate the effectiveness of our methodology.

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Cite

Text

Bin Faruk et al. "Accounting for Spatial Variability with the Histogram of Oriented Gradients Based Masking Improves Performance of Masked Autoencoder over Hyperspectral Satellite Imagery (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35253

Markdown

[Bin Faruk et al. "Accounting for Spatial Variability with the Histogram of Oriented Gradients Based Masking Improves Performance of Masked Autoencoder over Hyperspectral Satellite Imagery (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/faruk2025aaai-accounting/) doi:10.1609/AAAI.V39I28.35253

BibTeX

@inproceedings{faruk2025aaai-accounting,
  title     = {{Accounting for Spatial Variability with the Histogram of Oriented Gradients Based Masking Improves Performance of Masked Autoencoder over Hyperspectral Satellite Imagery (Student Abstract)}},
  author    = {Bin Faruk, Tanjim and Matin, Abdul and Pallickara, Shrideep and Pallickara, Sangmi Lee},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29365-29367},
  doi       = {10.1609/AAAI.V39I28.35253},
  url       = {https://mlanthology.org/aaai/2025/faruk2025aaai-accounting/}
}