Multispectral Model

Multispectral models analyze data from multiple wavelengths of light to extract richer information than traditional single-band imaging. Current research focuses on improving image fusion techniques (e.g., using neural radiance fields and implicit neural representations), addressing challenges in data acquisition and processing (like boresight rectification for push-broom sensors and demosaicing for filter array cameras), and developing robust deep learning architectures (including transformers and convolutional neural networks) for tasks such as semantic segmentation and object detection. These advancements are significantly impacting fields like remote sensing, where improved land cover mapping and wildfire management are key applications, and also enhancing other areas such as pedestrian detection and face recognition.