Enhancing Remote Sensing Image Quality through Data Fusion and Synthetic Aperture Radar (SAR): A Comparative Analysis of CNN, Lightweight ConvNet, and VGG16 Models

Authors

  • Desynike Puspa Anggreyni Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
  • Indriatmoko Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
  • Aniati Murni Arymurthy Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
  • Andie Setiyoko Research Center for Remote Sensing, Indonesia National Research and Innovation Agency, Jakarta, Indonesia

DOI:

https://doi.org/10.15575/join.v9i2.1321

Keywords:

Data Fusion, Image Classification, Synthetic Aperture Radar (SAR)

Abstract

Remote sensing technology benefits many parties, especially for carrying out land surveillance with comprehensive coverage without needing to move the equipment close to photograph the area. However, this technology needs to improve: the image quality depends on natural conditions, so objects such as fog, clouds, and smoke can interfere with the image results. This study uses data fusion techniques to enhance the quality of remote-sensing images affected by natural conditions. The method involves using Synthetic Aperture Radar (SAR) to combine adjacent satellite images from different viewpoints, thereby improving image coverage. Three image classification models were evaluated to process the fused data: Convolutional Neural Network (CNN), Lightweight ConvNet, and Visual Geometry Group 16 (VGG16). The results indicate that all three models achieve similar accuracy and execution speed, namely 0.925, with VGG16 demonstrating a slight superiority over the others, namely 0.90.

Author Biographies

Desynike Puspa Anggreyni, Faculty of Computer Science, Universitas Indonesia, Depok

Desynike Puspa Anggreyni, bachelor of material science, Lambung Mangkurat University. Currently a Magister Student of Computer Science, University of Indonesia, focused in deep learning, remote sensing, pattern recognition, beside that she strives to adapt her physics knowledge into the technology in remote sensing area using ARCGis, and QGIS software, she also has a strong knowledge in material science, especially batteries, semiconductor, and nanotechnology.

Indriatmoko, Faculty of Computer Science, Universitas Indonesia, Depok

indriatmoko, an Electrical Engineering graduate, excelled as a Project Engineer before advancing to a Project Management role. His career has been marked by leading complex engineering projects and a strong problem-solving acumen. Currently, indriatmoko is pursuing a Master's degree in Computer Science, integrating his engineering expertise with emerging digital technologies. This transition highlights his commitment to continuous learning and adapting to the dynamic tech landscape

Aniati Murni Arymurthy, Faculty of Computer Science, Universitas Indonesia, Depok

Aniati Murni Arymurthy is currently a Professor of computer science with the Faculty of Computer Science, Universitas Indonesia. She is an Expert in image processing and spatial data. Her skills and expertise are in classifications, feature extraction, algorithms, feature selection, algebra, genetic algorithms, image enhancement, wavelet, and self-organizing maps. She has published a large number of scientific articles in her fields.

Andie Setiyoko, Research Center for Remote Sensing, Indonesia National Research and Innovation Agency, Jakarta

Andie Setiyoko, Bachelor of Engineering in Geodetic Engineering, Bandung Institute of Technology (ITB), Master of Technology in Remote Sensing & GIS of Indian Institute of Remote Sensing (IIRS), and Dr. in Computer Science (University of Indonesia). He is a researcher at the Research Centre for Remote Sensing at The National Research and Innovation Agency (BRIN), with expertise in remote sensing data processing technology. He is also a SAR Technology and 3D Modeling Research Lab Group Leader

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2024-12-27

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