Object Based Spatial Unmixing Model

Object-based spatial unmixing models aim to decompose mixed signals, such as pixels in hyperspectral images or events in physiological recordings, into their constituent components. Current research emphasizes deep learning approaches, including autoencoders and transformers, often incorporating auxiliary tasks like super-resolution or denoising to improve accuracy and robustness. These advancements are impacting diverse fields, from remote sensing and medical imaging (e.g., brain tumor surgery) to speech recognition and material science, by enabling more accurate and efficient analysis of complex, multi-component data. The development of efficient algorithms and readily available datasets is driving progress in this area.