The binary partition tree (BPT) is a hierarchical data-structure that
models the content of an image in a multiscale way. In particular,
a cut of the BPT of an image provides a segmentation, as a partition
of the image support. Actually, building a BPT allows for
dramatically reducing the search space for segmentation purposes,
based on intrinsic (image signal) and extrinsic (construction metric)
information. A large literature has been devoted to the construction
on such metrics, and the associated choice of criteria (spectral, spatial,
geometric, etc.) for building relevant BPTs, in particular in the
challenging context of remote sensing. But, surprisingly, there exists
few works dedicated to evaluate the quality of BPTs, i.e. their
ability to further provide a satisfactory segmentation. In this paper,
we propose a framework for BPT quality evaluation, in a supervised
paradigm. Indeed, we assume that ground-truth segments
are provided by an expert, possibly with a semantic labelling and a
given uncertainty. Then, we describe local evaluation metrics, BPT
nodes / ground-truth segments fitting strategies, and global quality
score computation considering semantic information, leading to a
complete evaluation framework. This framework is illustrated in the
context of BPT segmentation of multispectral satellite images.