Fully Blind Hyperspectral Unmixing Without Explicit Mixing Models or Endmember Initialization

Autoencoder-based methods have gained prominence in blind hyperspectral unmixing (BHU), with most approaches based on predefined mixing assumptions, such as the linear mixing model (LLM). However, real-world scenes often involve complex nonlinear light-material interactions that are difficult to model explicitly, leading to systematic biases in endmember and abundance estimation when these assumptions deviate from the actual mixing mechanism. In this article, we extend conventional autoencoder-based BHU by jointly and adaptively learning the mixing mechanism directly from hyperspectral images (HSIs) while estimating endmembers and abundances, and propose a novel BHU framework termed unmixing Transformer (UNTR). In particular, an adaptive mixing mechanism modeling module is designed to parameterize the mixing mechanism, in which a specially designed transformer fuses heterogeneous endmember and abundance information. Furthermore, we develop an endmember query mechanism integrated into the adaptive mixing mechanism modeling module under a distribution alignment constraint, enabling robust endmember estimation under random initialization. As the first attempt to adaptively model the mixing mechanism as data-driven network parameters, UNTR achieves remarkable accuracy in both abundance and endmember estimation. Without relying on predefined mixing assumptions and well-initialized endmembers, UNTR exhibits strong adaptability to complex real-world scenarios. Our code will be available at https://github.com/ Preston-Dong/UNTR.