FSRSI: New Deep Learning-Based Approach for Super-Resolution of Multispectral Satellite Images.

Soufi, Omar; Belouadha, Fatima Zahra

Open access in space remote sensing has allowed easy access to satellite imagery; however, access to high-resolution imagery is not given to everyone, but only to those who master space technology. Thus, this paper presents a new approach for improving the quality of Sentinel-2 satellite images by super-resolution exploiting deep learning techniques. In this context, this work proposes a generic solution that improves the spatial resolution from 10m to 2.5m (scaling factor 4) taking into account the constraints of volumetry and dependence between spectral bands imposed by the specificities of satellite images. This study proposes the FSRSI model which exploits the potential of deep convolutional networks (CNN) and integrates new state-of-the-art concepts including Network in Network, end-to-end learning, multi-scale fusion, neural network optimization, acceleration, and filter transfer. This model has also been improved by an efficient mosaicking technique for the Super-Resolution of satellite images in addition to the consideration of inter-spectral dependence combined with the efficient choice of training data. This approach shows better performance than what has been proven in the field of spatial imagery. The experimental results showed that the adopted algorithm restores the details of satellite images quickly and efficiently; outperforming several state-of-the-art methods. These performances were observed following a benchmark with several neural networks and experimentation of applications to a carefully constructed dataset. The proposed solution showed promising results in terms of visual and perceptual quality with a better inference speed.

DEEP learning; REMOTE-sensing images; CONVOLUTIONAL neural networks

Ingénierie des Systèmes d'Information, 2023, Vol 28, Issue 1, p113

1633-1311

Academic Journal

10.18280/isi.280112