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SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation
Chih-Wei Lin1,2,3,4,5, Xiuping Huang1, Mengxiang Lin1
1College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|January 22, 2022
Summary
This study introduces a novel Signal Filtering Convolutional Neural Network (SF-CNN) for accurate precipitation intensity estimation from surveillance images. The SF-CNN effectively filters noise and extracts key features, outperforming existing methods.
Area of Science:
- Computer Vision
- Meteorology
- Machine Learning
Background:
- Precipitation intensity estimation is crucial for weather analysis.
- Existing methods often use complex models for rain streak extraction, but efficient estimation from surveillance cameras remains a challenge.
Purpose of the Study:
- To propose an efficient convolutional neural network (CNN) for estimating precipitation intensity from surveillance images.
- To introduce a novel Signal Filtering Convolutional Neural Network (SF-CNN) designed for this task.
Main Methods:
- Developed the Signal Filtering Convolutional Neural Network (SF-CNN) comprising a Signal Filtering (SF) block and a Gradually Decreasing Dimension (GDD) block.
- The SF block removes noise from rain streak features, while the GDD block reduces feature dimensions while preserving information.
Main Results:
- The SF-CNN demonstrated effectiveness in precipitation intensity estimation using a self-collected dataset of 9394 raining images across six intensity levels.
- Experimental results showed the proposed approach outperformed popular convolutional neural networks.
Conclusions:
- The proposed SF-CNN offers an effective solution for precipitation intensity estimation from surveillance imagery.
- The study introduced the largest dataset to date for monitoring infrared images of precipitation intensity.
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