Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

Developing a robust algorithm for automatic individual tree crown (ITC) detection from
airborne laser scanning (ALS) data sets is important for tracking the responses of trees to …

Spectral clustering algorithms typically require a priori selection of input parameters such as
the number of clusters, a scaling parameter for the affinity measure, or ranges of these …

This paper proposes and analyzes a novel clustering algorithm, called learning by
unsupervised nonlinear diffusion (LUND), that combines graph-based diffusion geometry …

The recognition of spatial patterns within agricultural fields, presenting similar yield potential
areas, stable through time, is very important for optimizing agricultural practices. This study …

Clustering algorithms partition a dataset into groups of similar points. The clustering problem
is very general, and different partitions of the same dataset could be considered correct and …

Imaging spectroscopy is a powerful tool for mapping and monitoring the spatial distribution
of species compositions. Most spectroscopy studies rely on extensive field campaigns to …

This paper proposes and analyzes a novel clustering algorithm that combines graph-based
diffusion geometry with techniques based on density and mode estimation. The proposed …

Developing a robust algorithm for automatic individual tree crown (ITC) detection from
airborne laser scanning datasets is important for tracking the responses of trees to …

We propose a method for the unsupervised clustering of hyperspectral images based on
spatially regularized spectral clustering with ultrametric path distances. The proposed …