A Hybrid Spatial Fuzzy Inference and CNN Approach for Forecasting Changes in Remote Sensing
Year: 2024
Type: article
Abstract: Satellite cloud 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 in terms of both root mean square error (RMSE) and R-squared (R2) metrics.
Author: Minh Hoang Le
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4403174879