Hyperspectral unmixing allows to represent mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image is represented as a high-order tensor with additional features in a multimodal, multi-feature framework. Tensor models such as canonical polyadic decomposition allow for this kind of unmixing, but lack a general framework and interpretability of the results. In this paper, we propose a methodological framework for multi-feature unmixing based on alternating optimization alternating direction method of multipliers and incorporating abundance sum-to-one constraint (AO-ADMM-ASC), with in-depth mathematical, physical and graphical interpretation and connections with the extended linear mixing model. As additional features, we propose to incorporate mathematical morphology and reframe a previous work on neighborhood patches within our framework. Experiments on real hyperspectral image data show the efficiency of AO-ADMM-ASC and allows an in-depth interpretation of the model. Python and MATLAB implementations of AO-ADMM-ASC are made available at: https://github.com/mhmdjouni/AoAdmmAsc-python https://github.com/mhmdjouni/AoAdmmAsc-matlab