A Hybrid Spatial Fuzzy Inference and CNN Approach for Forecasting Changes in Remote Sensing

  • Minh Hoang Le Ha Noi University of Industry, Hanoi, Vietnam / Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Nguyen Long Giang Institute of Information Technology, Viet Nam Academy of Science and Technology
  • Nguyen Van Thien Ha Noi University of Industry, Hanoi, Vietnam
  • Nguyen Van Luong Ha Noi University of Industry, Hanoi, Vietnam
  • Luong Thi Hong Lan Ha Noi University of Industry, Hanoi, Vietnam
Keywords: Convolutional neural networks, Spatial complex fuzzy inference system, change detection, Remote sensing images

Abstract

Satellite images, captured by Earth observation satellites, provide crucial information for weather forecasting, aviation, natural resource management, and disaster response. This study presents a novel method for detecting changes in satellite cloud images, combining convolutional neural networks (CNNs) for feature extraction with a spatial complex fuzzy inference system (CFIS). The CNNs effectively capture color channel features in the image blurring and deblurring processes, while the CFIS models the spatiotemporal relationships in the data. Experiments conducted on a US Navy satellite image dataset demonstrate the effectiveness and stability of our proposed method, outperforming related studies
concerning of R-squared (R2) and root mean square error (RMSE) measures.

Author Biographies

Minh Hoang Le, Ha Noi University of Industry, Hanoi, Vietnam / Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam

LE MINH HOANG received the Master’s degree on University of Engineering and Technology, Vietnam National University in 2007. In June 2024, he enrolled as a PhD student at the Institute of Information Technology (IoIT), Vietnam Academy of Science and Technology, Ha Noi, Vietnam. His interests include Optimization, Machine learning, Data mining

Nguyen Long Giang, Institute of Information Technology, Viet Nam Academy of Science and Technology

NGUYEN LONG GIANG earned the Ph.D. degree in mathematics in 2012. His is currently an Associate Professor in the Institute of Information Technology, Vietnam Academy of Science and Technology. His research interests include artificial intelligence, data mining, soft computing, and fuzzy computing.

Nguyen Van Thien, Ha Noi University of Industry, Hanoi, Vietnam

NGUYEN VAN THIEN has been graduated from Hanoi University of Technology in 1996 and received a PhD Degree in Information Systems in Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam, in 2018. He is currently Vice Principal of Hanoi University of Industry. His interests
include Optimization, Machine learning, Data mining, Artificial Intelligence, Soft Computing, and Fuzzy Computing.

Nguyen Van Luong, Ha Noi University of Industry, Hanoi, Vietnam

NGUYEN VAN LUONG has been graduated in Information Technology at Hanoi University of Industry in 2020. Then, he received a master’s degree at the Institute of Information Technology, Vietnam Academy of Science and Technology, in 2023. He works at the Center of Information Technology, Hanoi University of Industry. His
research focuses on Artificial Intelligence, Machine learning, Soft computing, etc.

Luong Thi Hong Lan, Ha Noi University of Industry, Hanoi, Vietnam

LUONG THI HONG LAN has been graduated from Hanoi University of Technology in 2002 and received a PhD. Degree in Computer Science in Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam, in 2021. Now, she is working at the Faculty of Information Technology at Hanoi
University of Industry. Her research concentrates on Artificial Intelligence, Data Mining, Soft Computing, and Fuzzy Computing.

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Published
2024-11-25