Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks

Julian Supardi (Sriwijaya University)
Samsuryadi Samsuryadi (Sriwijaya University)
Hadipurnawan Satria (Sriwijaya University)
Philip Alger M. Serrano (College of Computer Studies Camarines Sur Polytechnic Colleges)
Arnelawati Arnelawati (Sriwijaya University)

Jurnal Elektronika dan Telekomunikasi
Vol. 24 No. 2 (2024)

Remote sensing imagery is a very interesting topic for researchers, especially in the fields of image and pattern recognition. Remote sensing images differ from ordinary images taken with conventional cameras. Remote sensing images are captured from satellite photos taken far above the Earth's surface. As a result, objects in satellite images appear small and have low resolution when enlarged. This condition makes it difficult to detect and recognize objects in remote-sensing images. However, detecting and recognizing objects in these images is crucial for various aspects of human life. This paper aims to address the problem of remote sensing image quality. The method used is a convolutional neural network. The results show the proposed method can improve PSNR and SSIM compared to previous methods