Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

Abstract

Inland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images.

Keywords

• super-resolution
• convolutional neural network (cnn)
• generative adversarial network (gan)
• remote sensing
• chlorophyll-a