Remote sensing image regression for heterogeneous change detection

TL;DR

This paper introduces a regression-based framework for change detection in heterogeneous multitemporal satellite images, comparing four regression methods to evaluate their effectiveness and robustness.

Contribution

It proposes a novel image regression framework for heterogeneous change detection and systematically compares four regression methods within this framework.

Findings

Random forests are fast, robust, and perform well.

Homogeneous pixel transformation achieves higher accuracy but is more complex.

The framework effectively models transformations between heterogeneous images.

Abstract

Change detection in heterogeneous multitemporal satellite images is an emerging topic in remote sensing. In this paper we propose a framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, which has become a main topic in remote sensing. Our method learns a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian processes, support vector machines, random forests, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate not only potentials and limitations of our framework, but also the pros and cons of each regression method, we perform experiments on two data sets. The results indicates that random forests achieve good performance, are fast and robust to hyperparameters, whereas the homogeneous pixel transformation method can achieve better accuracy at the cost of a higher complexity.