Remote sensing image regression for heterogeneous change detection
This work addresses a domain-specific problem in remote sensing for analyzing satellite image changes, presenting an incremental improvement by comparing existing regression methods.
The paper tackles change detection in heterogeneous multitemporal satellite images by proposing an image regression framework that learns transformations between images using four methods, with results showing random forests offer good performance and speed while homogeneous pixel transformation achieves higher accuracy at greater complexity.
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.