With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications.

中文翻译：

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知识蒸馏在遥感中的应用：一项调查


随着遥感 （RS） 领域模型的复杂性不断增加，对平衡模型精度和计算效率的解决方案的需求越来越大。知识蒸馏 （KD） 已成为满足这一需求的强大工具，它能够将知识从大型、复杂的模型转移到更小、更高效的模型，而不会显著降低性能。这篇综述文章对 KD 及其在 RS 中的创新应用进行了广泛的研究。KD 是一种将知识从复杂、通常繁琐的模型（教师）转移到更紧凑、更高效的模型（学生）的技术，在各个领域都取得了重大发展和应用。首先，我们介绍了 KD 方法的基本概念和历史进展。强调了使用 KD 的优势，特别是在模型压缩、增强计算效率和改进性能方面，这对于 RS 场景中的实际部署至关重要。本文提供了 KD 技术的全面分类法，其中对每个类别进行了批判性分析，以展示替代选项的广度和深度，并说明了展示 KD 方法在 RS 任务中实际实施的具体案例研究，例如实例分割和对象检测。此外，本综述讨论了 KD 在 RS 中的挑战和局限性，包括实际限制和前瞻性的未来方向，为 RS 领域的研究人员和从业者提供了全面的概述。 通过这个组织，该论文不仅阐明了 KD 研究的现状，还为未来的研究机会奠定了基础，从而为学术研究和实际应用做出了重大贡献。