In the context of digital image processing and analysis, the Binary Partition Tree (BPT) is a classical data-structure for the hierarchical modelling of images at different scales. BPTs belong both to the families of graph-based models and morphological hierarchies. They constitute an efficient way to define sets of nested partitions of image support, that further provide knowledge-guided reduced research spaces for optimization-based segmentation procedures. Basically, a BPT is built in a mono-feature way, i.e., for one given image, and one given metric, by merging pairs of connected image regions that are similar in the induced feature space. We propose in this work a generalization of the BPT construction framework, allowing to embed multiple features. The cornerstone of our approach relies on a collaborative strategy enabling to establish a consensus between different metrics, thus allowing to obtain a unified hierarchical segmentation space. In particular, this provides alternatives to the complex issue of arbitrary metric construction from several – possibly non-comparable – features. To reach that goal, we first revisit the BPT construction algorithm to describe it in a fully graph-based formalism. Then, we present the structural and algorithmic evolutions and impacts when embedding multiple features in BPT construction. We also discuss different ways to tackle the induced memory and time complexity issues raised by this generalized framework. Final experiments illustrate how this multi-feature framework can be used to build BPTs from multiple metrics computed through the (potentially multiple) image content(s), in particular in the context of remote sensing.