Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks

Supardi, Julian and Samsuryadi, Samsuryadi and Satria, Hadipurnawan and Serrano, Philip Alger M and Arnelawati, arnelawati (2024) Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks. Jurnal Elektronika dan Telekomunikasi, 24 (2). pp. 112-119. ISSN 1411-8289

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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. Our proposed method consists of two main parts: the first part focuses on feature extraction, and the second part is dedicated to image reconstruction. The feature extraction component includes 25 convolutional layers, whereas the reconstruction component comprises 75 convolutional layers. To validate the effectiveness of our proposed method, we employed the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as evaluation metrics. The test datasets consisted of Landsat-8 images, which were segmented into three regions of interest (ROI) of sizes 16×16 pixels, 24×24 pixels, and 32×32 pixels. The experimental results demonstrate that the PSNR/SSIM values achieved were 28.94/0.822, 30.24/0.089, and 33.24/0.925 for each respective ROI. These results indicate that the proposed method outperforms several state-of-the-art techniques in terms of PSNR and SSIM.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75.5 Mathematics--Periodicals. Computer engineering. Computer science
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Julian Supardi
Date Deposited:	06 Feb 2025 03:16
Last Modified:	06 Feb 2025 03:16
URI:	http://repository.unsri.ac.id/id/eprint/165217

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