scholarly journals A Spectral Evaluation of the Application of Super‐Resolution to Commercial Satellite Imagery, STL-010-18, Year 3

2020 ◽  
Author(s):  
Johanna Turk ◽  
Carmelo Gonzales ◽  
Eric Moore
Keyword(s):  
Satellite Imagery ◽  
Super Resolution ◽  
Spectral Evaluation

Video Satellite Imagery Super Resolution via Convolutional Neural Networks

2017 ◽  
Vol 14 (12) ◽  
pp. 2398-2402 ◽  
Author(s):  
Yimin Luo ◽  
Liguo Zhou ◽  
Shu Wang ◽  
Zhongyuan Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Satellite Imagery ◽  
Super Resolution

scholarly journals Automatic Generation of Aerial Orthoimages Using Sentinel-2 Satellite Imagery with a Context-Based Deep Learning Approach

2021 ◽  
Vol 11 (3) ◽  
pp. 1089
Author(s):  
Suhong Yoo ◽  
Jisang Lee ◽  
Junsu Bae ◽  
Hyoseon Jang ◽  
Hong-Gyoo Sohn
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Mean Squared Error ◽  
Similarity Index ◽  
Super Resolution ◽  
Structural Similarity ◽  
Automatic Generation ◽  
Fine Tuning ◽  
Aerial Images ◽  
Sentinel 2

Aerial images are an outstanding option for observing terrain with their high-resolution (HR) capability. The high operational cost of aerial images makes it difficult to acquire periodic observation of the region of interest. Satellite imagery is an alternative for the problem, but low-resolution is an obstacle. In this study, we proposed a context-based approach to simulate the 10 m resolution of Sentinel-2 imagery to produce 2.5 and 5.0 m prediction images using the aerial orthoimage acquired over the same period. The proposed model was compared with an enhanced deep super-resolution network (EDSR), which has excellent performance among the existing super-resolution (SR) deep learning algorithms, using the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-squared error (RMSE). Our context-based ResU-Net outperformed the EDSR in all three metrics. The inclusion of the 60 m resolution of Sentinel-2 imagery performs better through fine-tuning. When 60 m images were included, RMSE decreased, and PSNR and SSIM increased. The result also validated that the denser the neural network, the higher the quality. Moreover, the accuracy is much higher when both denser feature dimensions and the 60 m images were used.

Sign in / Sign up

Export Citation Format

Share Document