The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …

Morphological reconstruction (MR) is often employed by seeded image segmentation
algorithms such as watershed transform and power watershed, as it is able to filter out seeds …

Due to digitization, usual discrete signals generally present topological paradoxes, such as
the connectivity paradoxes of Rosenfeld. To get rid of those paradoxes, and to restore some …

In this paper, we propose a hierarchical building detection framework based on deep
learning model, which focuses on accurately detecting buildings from remote sensing …

This paper discusses an image processing architecture and tools to address the problem of
ship detection in synthetic-aperture radar images. The detection strategy relies on a tree …

This paper aims to understand the practical features of hierarchies of morphological
segmentations, namely the quasi-flat zones hierarchy and watershed hierarchies, and to …

Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as
connected areas instead of single pixels or subpixels, both spatial and spectral information …

Topology is a classical branch of mathematics, born essentially from Euler's studies in the
XVII century, which deals with the abstract notion of shape and geometry. Last decades …

Supervised semantic segmentation algorithms have been a hot area of exploration recently,
but now the attention is being drawn towards completely unsupervised semantic …