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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar Jurnal Elektronika dan Telekomunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Computer Science and Information Technologies Sriwijaya Journal of Informatics and Applications Jurnal Abdi Masyarakat Indonesia Jurnal Abdimas Maduma
Hadipurnawan Satria
Department Of Informatics, Faculty Of Computer Science, Universitas Sriwijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Journal : Jurnal Elektronika dan Telekomunikasi

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Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks Supardi, Julian; Samsuryadi, Samsuryadi; Satria, Hadipurnawan; Serrano, Philip Alger M.; Arnelawati, Arnelawati
Jurnal Elektronika dan Telekomunikasi Vol 24, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.653

Abstract

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
Co-Authors Abdurahman Ahmad Nizar Ahmad Zarkasi Anggun Islami Arnaldo, Muhammad Arnelawati, Arnelawati Deris Stiawan Dian Palupi Rini Ermatita - Hasby Rifky Heru Saputra Iskandar, Ananda Haykel Islami, Anggun Jambak, Muhammad Ihsan Julian Supardi M. Nazhif Rizani Mohammad Eko Fitrianto Muhammad Afif Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Naufal Rachmatullah Muhammad Qurhanul Rizqie Muhammad Qurhanul Rizqie Nugraha, Wibisena Nuzulastri, Sari Osvari Arsalan Primanita, Anggina Rahmat Budiarto Richardo, M Denny Rifkie Primartha Rizki Kurniati Samsuryadi Sahmin Samsuryadi Samsuryadi Serrano, Philip Alger M. Siti Nurmaini Sutarno Sutarno Syahroyni, Muhammad Widya Aprilini Yos Karimudin