Maintaining original color when applying super-resolution to individual bands

Bingcai Zhang; Yen Luu

doi:10.1117/12.2670602

4 October 2023 Maintaining original color when applying super-resolution to individual bands

Bingcai Zhang, Yen Luu

Proceedings Volume 12675, Applications of Machine Learning 2023; 1267504 (2023) https://doi.org/10.1117/12.2670602
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

We have developed a system that applies deep learning application super-resolution (SR) to multispectral and hyperspectral geospatial satellite imagery to deduce higher resolution images from lower resolution images while maintaining the original color of the lower resolution pixels. A super-resolution model, which uses Deep Convolution Neural Networks (DCNNs), is trained using individual image bands, a large crop size or tile size of 512 × 512 pixels, and a de-noise algorithm. Applying our algorithms to maintain the original color of the image bands improves the quality metrics of the super-resolution images as measured by peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of super-resolution images. One of the most important applications of satellite images is to automatically detect small objects such as vehicles and small boats. With super-resolution images generated by our system, the object detection accuracy (recall and precision) has improved by 20% with Planet® multispectral satellite images.

Conference Presentation

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

Citation Download Citation

Bingcai Zhang and Yen Luu "Maintaining original color when applying super-resolution to individual bands", Proc. SPIE 12675, Applications of Machine Learning 2023, 1267504 (4 October 2023); https://doi.org/10.1117/12.2670602

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