The article discusses the significant advancements in few-shot learning techniques applied to remote sensing, emphasizing their ability to classify hyperspectral images and other data types with minimal training examples. It highlights the importance of integrating explainable AI to enhance the understanding of classification processes, ensuring transparency and trust in automated systems. Focused on satellite and UAV data, the review presents a comprehensive overview of current methodologies, evaluations, and the potential for future research directions in the intersection of machine learning and remote sensing.
Few-shot learning techniques are transformative in remote sensing, enhancing the capability to classify images from multiple sensors with minimal training data, aiding in real-time analytics.
The integration of explainable AI in remote sensing models provides transparency in classification results, fostering greater trust in automated decision-making across diverse applications of remote sensing.
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