Land cover classification of satellite images using deep learning

Md Sami Ul Hoque; Al Mahmud; Roshan Silwal; Hanieh Ajami; Mahdi Kargar Nigjeh; Scott E. Umbaugh

doi:10.1117/12.3028330

30 September 2024 Land cover classification of satellite images using deep learning

Md Sami Ul Hoque, Al Mahmud, Roshan Silwal, Hanieh Ajami, Mahdi Kargar Nigjeh, Scott E. Umbaugh

Author Affiliations +

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131370L (2024) https://doi.org/10.1117/12.3028330
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

The advent of high-resolution satellite imagery has revolutionized remote sensing applications, offering unprecedented access and detail worldwide. However, the vast volume and complexity of this data pose significant challenges for traditional classification methods, necessitating advanced computational approaches. This research leverages the OpenEarthMap dataset, an extensive and diverse collection of high-resolution earth observation imagery, to improve landcover classification accuracy using deep learning techniques. We optimize a U-Net convolutional neural network architecture by analyzing different learning rates and variations on the OpenEarthMap dataset and choose this architecture because of its previous success in semantic segmentation tasks. Through a comprehensive methodology encompassing dataset preparation, preprocessing, network parameter experimentation, and model evaluation, this research seeks to push the boundaries of current landcover classification capabilities and contribute valuable insights to the field of remote sensing. Using a Unet on 3500 OpenEarthMap images, Adam optimizer, and image variation preprocessing, we found an f-score of 0.75, consistent with visual image interpretation. Future work involving additional images, a 4x image split implementation, and other models may improve this f-score and the associated semantic segmentation.

Conference Presentation

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

Citation Download Citation

Md Sami Ul Hoque, Al Mahmud, Roshan Silwal, Hanieh Ajami, Mahdi Kargar Nigjeh, and Scott E. Umbaugh "Land cover classification of satellite images using deep learning", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131370L (30 September 2024); https://doi.org/10.1117/12.3028330

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