TY - GEN
T1 - Super-resolution on remote sensing images
AU - Yang, Yuting
AU - Lam, Kin Man
AU - Dong, Junyu
AU - Sun, Xin
AU - Jian, Muwei
N1 - Funding Information:
This paper was supported by The Hong Kong Polytechnic University, Hong Kong, under the project SBoS.
Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2021/3
Y1 - 2021/3
N2 - High-resolution ocean remote sensing images are of vital importance in the research field of ocean remote sensing. However, the available ocean remote sensing images are composed of averaged data, whose resolution is lower than the instant remote sensing images. In this paper, we propose a very deep super-resolution learning model for remote-sensing image super-resolution. In our research, we target satellite-derived sea surface temperature (SST) images, a typical kind of ocean remote sensing image, as a specific case study of super-resolution on remote sensing images. In this paper, we propose a novel model architecture based on the very deep super-resolution (VDSR) model, to further enhance its performance. Furthermore, we evaluate the peak signal-to-noise ratio (PSNR) and perceptual loss of the model trained on the natural images and SST frames. We designed and applied our model to the China Ocean SST database, the Ocean SST database, and the Ocean-Front databases, all containing remote sensing images captured by advanced very high resolution radiometers (AVHRR). Experimental results show that our model performs better than the state-of-the-art models on SST frames.
AB - High-resolution ocean remote sensing images are of vital importance in the research field of ocean remote sensing. However, the available ocean remote sensing images are composed of averaged data, whose resolution is lower than the instant remote sensing images. In this paper, we propose a very deep super-resolution learning model for remote-sensing image super-resolution. In our research, we target satellite-derived sea surface temperature (SST) images, a typical kind of ocean remote sensing image, as a specific case study of super-resolution on remote sensing images. In this paper, we propose a novel model architecture based on the very deep super-resolution (VDSR) model, to further enhance its performance. Furthermore, we evaluate the peak signal-to-noise ratio (PSNR) and perceptual loss of the model trained on the natural images and SST frames. We designed and applied our model to the China Ocean SST database, the Ocean SST database, and the Ocean-Front databases, all containing remote sensing images captured by advanced very high resolution radiometers (AVHRR). Experimental results show that our model performs better than the state-of-the-art models on SST frames.
UR - http://www.scopus.com/inward/record.url?scp=85103277844&partnerID=8YFLogxK
U2 - 10.1117/12.2590197
DO - 10.1117/12.2590197
M3 - Conference article published in proceeding or book
AN - SCOPUS:85103277844
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - International Workshop on Advanced Imaging Technology, IWAIT 2021
A2 - Nakajima, Masayuki
A2 - Kim, Jae-Gon
A2 - Lie, Wen-Nung
A2 - Kemao, Qian
PB - SPIE
T2 - 2021 International Workshop on Advanced Imaging Technology, IWAIT 2021
Y2 - 5 January 2021 through 6 January 2021
ER -