A comparative noise robustness study of tree representations for attribute profile construction

Abstract

Morphological attribute profiles are among the most prominent spatial-spectral pixel description tools. They can be calculated efficiently from tree based representations of an image. Although mostly implemented with inclusion trees (i.e. component trees and tree of shapes), attribute profiles have been recently adapted to partitioning trees, and specifically alpha- and omega-trees. Partitioning trees constitute a more flexible option especially when dealing with multivariate data. This work explores the noise robustness of the aforementioned major tree types in terms of pixel classification performance of the resulting attribute profiles, and presents our preliminary findings that support the use of partitioning trees as a basis for attribute profile construction.