ALGA-DenseNet ground-based cloud classification network based on multi-scale features
Binbin Tu, Haoyuan Zhou, Xiaowei Han, Jiawei Bao, Linfei Zhao, Nanmu Hui
TL;DR
This paper presents ALGA-DenseNet, a new cloud classification model that improves accuracy and efficiency for ground-based cloud recognition.
Contribution
The novel ALGA-DenseNet model combines multi-scale attention and vision transformers for enhanced cloud classification.
Findings
ALGA-DenseNet achieved 97.94% accuracy on the TJNU Ground-based Cloud Dataset.
The model also reached 97.25% accuracy on the CCSN dataset.
It effectively extracts cloud features with reduced computational complexity.
Abstract
Automatic recognition of ground-based clouds is crucial for meteorology and especially for the operational safety of Unmanned Aerial Vehicles (UAVs), but it is challenged by variable cloud shapes, complex lighting, and background interference. This paper introduces ALGA-DenseNet, an improved DenseNet model with a multi-scale attention mechanism. The model employs Color Jitter to enhance image robustness and improve learning of intra-class variations and inter-class differences. It incorporates Adaptive Local and Global Attention (ALGA) to merge features, enhancing feature selection. Additionally, it integrates mixed and depthwise separable convolutions to optimize multi-scale feature extraction, reducing parameters and computational complexity. Furthermore, integrating a Vision Transformer (ViT) and Dynamic Multi-head Attention (DMA) enhances representation of complex cloud features.…
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Taxonomy
TopicsSolar Radiation and Photovoltaics · Advanced Neural Network Applications · Remote Sensing in Agriculture