Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study

TL;DR

This paper compares the effectiveness of attribute profiles and feature profiles, generated from various tree structures, for classifying remote sensing images, demonstrating the superior performance of feature profiles across different image types.

Contribution

It provides a comprehensive comparison of attribute and feature profiles derived from multiple tree structures for remote sensing image classification.

Findings

Feature profiles outperform attribute profiles in classification accuracy.

Experimental results confirm the efficiency of feature profiles on diverse image types.

Different tree structures influence the performance of profile-based classification.

Abstract

The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures. Over the past few years, APs have been among the most effective methods to model the image's spatial and contextual information. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from component trees (max-tree and min-tree). In this work, we investigate their performance on the inclusion tree (tree of shapes) and partition trees (alpha tree and omega tree). Experimental results from both panchromatic and hyperspectral images again confirm the efficiency of FPs compared to APs.