Multispectral Image

Multispectral imaging captures information across multiple wavelengths of the electromagnetic spectrum, extending beyond the visible range to provide richer data for various applications. Current research emphasizes improving the accuracy and efficiency of object detection and classification within multispectral images, often leveraging deep learning architectures like convolutional neural networks (CNNs) and transformers, along with advanced techniques such as pansharpening and data fusion. These advancements are significantly impacting fields like remote sensing, precision agriculture, and medical imaging by enabling more accurate and efficient analysis of complex scenes and improved automated decision-making. The development of large-scale datasets and self-supervised learning methods are also key areas of focus to address challenges related to data scarcity and computational cost.