The article discusses advancements in few-shot learning techniques applied to remote sensing, emphasizing their effectiveness in classification tasks across various domains. Convolutional Neural Networks (CNN) remain prevalent, while graph-based and transformer methods are gaining traction for their superior spatial awareness in Synthetic Aperture Radar (SAR) and Very High Resolution (VHR) images. The insights provided into the effectiveness of these methodologies, particularly in contexts with limited labeled data, underline the potential for enhanced classification accuracy in remote sensing applications. These observations reveal both existing strengths and possible future research directions.
Convolution-based few-shot learning models are still popular for classification tasks in all three domains, adapting quickly to new classes with few examples.
Graph-based methods are becoming more popular for classifying SAR images, capturing essential spatial relationships for better accuracy in classification tasks.
#few-shot-learning #remote-sensing #image-classification #convolutional-neural-networks #graph-based-methods
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