UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation

TL;DR

This paper introduces UCloudNet, a residual U-Net with deep supervision, which improves cloud image segmentation accuracy and reduces training time for real-time meteorological applications.

Contribution

The novel residual U-Net architecture with deep supervision enhances cloud segmentation accuracy and training efficiency over previous methods.

Findings

Higher segmentation accuracy achieved.

Reduced training epochs needed.

Improved feature extraction with residual connections.

Abstract

Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and understanding atmospheric phenomena. Traditionally, cloud image segmentation relied on conventional computer vision techniques. However, with the advent of deep learning, convolutional neural networks (CNNs) are increasingly applied for this purpose. Despite their effectiveness, CNNs often require many epochs to converge, posing challenges for real-time processing in sky camera systems. In this paper, we introduce a residual U-Net with deep supervision for cloud segmentation which provides better accuracy than previous approaches, and with less training consumption. By utilizing residual connection in encoders of UCloudNet, the feature extraction ability is further improved.