Multi Spectral

Multi-spectral imaging utilizes data from multiple wavelengths of light to capture richer information than single-band imagery. Current research focuses on leveraging deep learning, particularly convolutional neural networks (CNNs) and transformers, to analyze this data for diverse applications, including object detection, segmentation, and quantitative estimations of environmental variables like biomass and pollution. These advancements are significantly improving the accuracy and efficiency of remote sensing, enabling more precise monitoring of environmental changes, resource management, and disaster response. The development of novel algorithms, including those incorporating frequency analysis and self-supervised learning, continues to push the boundaries of multi-spectral image processing.