Combining multiple resolutions into hierarchical representations for kernel-based image classification

TL;DR

This paper introduces a multiscale hierarchical image classification method that fuses multiple resolutions to improve geographic image analysis accuracy using structured kernels.

Contribution

It presents a novel hierarchical representation combining different resolution images and dedicated kernels for enhanced classification performance.

Findings

Significant accuracy improvement over single-scale methods

Effective fusion of multi-resolution data in hierarchical structure

Overcomes limitations of traditional GEOBIA classification

Abstract

Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with different sensors and are naturally fused together through the hierarchical representation, where coarser levels are built from a Low Spatial Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels are generated from a High Spatial Resolution (HSR) or Very High Spatial Resolution (VHSR) image. Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels. Two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation. This strategy overcomes the limits of conventional GEOBIA classification procedures that can handle only one or very few pre-selected scales. Experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w.r.t. conventional approaches working on a single scale.