Self-Supervised Multi-Scale Transformers for Fine-Grained Classification of Optical Remote Sensing
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Description
This chapter explores self-supervised multi-scale Transformer architectures for fine-grained classification of optical remote sensing imagery. It highlights the integration of self-supervised learning, multi-scale feature extraction, and attention mechanisms to improve image representation and classification. Self-supervised learning reduces dependence on labeled datasets, while multi-scale processing captures local details and global contextual information. Transformer architectures help identify spatial relationships and distinguish visually similar geographical categories. The chapter discusses fundamental concepts, potential methodologies, evaluation metrics, and applications in agriculture, urban planning, environmental monitoring, and land-cover mapping. It provides valuable insights for researchers in remote sensing, computer vision, and deep learning.