Deep learning based Landsat 8 OLI multi-band aerosol optical thickness inversion

Yixuan Xu; Lin Sun; Chen Jia; Chuang Han

doi:10.1117/12.3007532

30 August 2023 Deep learning based Landsat 8 OLI multi-band aerosol optical thickness inversion

Yixuan Xu, Lin Sun, Chen Jia, Chuang Han

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127970R (2023) https://doi.org/10.1117/12.3007532
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

This paper presents a deep learning based method for inversion of Aerosol Optical Depth (AOD) from Landsat 8 OLI multi-band satellite data. Traditional aerosol inversion algorithms include the dark target method, the deep blue algorithm and other algorithms. The dark target method is good for areas with low surface reflectivity such as dense vegetation and water bodies, while the deep blue algorithm is usually used for inversion in areas with high reflectivity. Therefore, this study employs Deep belief networks (DBN) to learn the potential relationship between Landsat 8 OLI multi-band data and AOD. In this paper, Landsat 8 OLI remote sensing images with atmospheric AOD observations over the past 6 years (2013-2018) were collected as the training dataset. A deep confidence network structure is next designed for learning the mapping relationships from Landsat 8 OLI multi-band data to AOD. To evaluate the performance of the proposed method, we used a test set for validation. The experimental results show that the proposed deep learning-based method has higher accuracy and stability in AOD inversion compared to traditional methods.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yixuan Xu, Lin Sun, Chen Jia, and Chuang Han "Deep learning based Landsat 8 OLI multi-band aerosol optical thickness inversion", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127970R (30 August 2023); https://doi.org/10.1117/12.3007532

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