This article discusses the enhancement of deep learning methods for image-based remote sensing, emphasizing the limitations posed by the need for extensive labeled datasets. Few-shot learning emerges as a promising solution, enabling model training with minimal labeled examples. The article reviews recent advances in few-shot techniques, particularly focusing on their application in object detection and segmentation within remote sensing. It highlights various evaluation metrics and datasets used for benchmarking these methods, as well as the role of Explainable AI in increasing the transparency of remote sensing applications. Future directions for research are considered as well.
Few-shot learning addresses the challenge of requiring large labeled datasets in remote sensing, making deep learning techniques more applicable in data-limited scenarios.
Although advancements have been made in image-based remote sensing tasks utilizing deep learning, there remains a need for efficient techniques that work with limited labeled data.
Recent few-shot learning techniques indicate a growing interest in object detection and segmentation in remote sensing, offering promising approaches to tackle data scarcity.
The integration of Explainable AI is essential to improve transparency in remote sensing applications, enhancing trust and understanding in autonomous decisions based on remote data analysis.
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