A CNN-based rescaling algorithm and performance analysis for spatial resolution enhancement of Landsat images

ABSTRACT

This study aimed to propose a new convolutional neural network (CNN)-based model for spatial resolution enhancement of Landsat images without finer resolution bands for training the CNN-based model. Its performance was assessed using Landsat 8 images from eight different regions that were dominated by various topographic features, and land use and land cover types by comparison with bicubic (BIC), nearest neighbor (NN) and Lanczos (LZ) resampling. It was found that for spatial resolution enhancement of 30 m resolution images to 15 m, 10 m and 7.5 m, the proposed model increased the average peak-signal-to-noise-ratio (PSNR) values by 1.7% to 11.5% compared with the compared methods. The PSNR increases were statistically significantly different from zero at the significant level of .05, but the improvement decreased as the spatial resolution of the input images became finer. Moreover, the deeper the CNN model, the better the performance, but after nine layers, the gain of performance slowed down. This indicates that the proposed algorithm is promising for spatial resolution enhancement of optical images without input of finer spatial resolution images.

Keywords:

• Optical image
• spatial resolution enhancement
• convolutional neural network
• layer depth
• land cover type

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the US Army Corps of Engineers, Construction Engineering Research Laboratory (USA-CERL) [W9132T-17-2-0009].