Deep learning processing of remotely sensed multi-spectral images

This chapter covers the most recent advancements in deep learning approaches tailored to multi-spectral remotely sensed images. Multi-spectral imaging conveys detailed information across several wavelengths, allowing for better environmental monitoring, precision agriculture, urban planning, and disaster management. The ability of deep learning-based approaches to extract complex patterns and features holds prospective in this domain. We specifically explore the challenges that these images give, including disparities in spatial resolution, spectral variability, and a lack of labelled data, while concurrently looking at cutting-edge deep learning-based algorithms and learning techniques specifically designed to deal with them. By summarizing current developments and outlining future research objectives, this chapter serves as a valuable resource for academics and professionals seeking to leverage deep learning for multi-spectral remote sensing image analysis.