Remote sensing image regression for heterogeneous change detection
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...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.1807.11766 |