  Change detection in heterogeneous multitemporal satellite images is an
emerging and challenging topic in remote sensing. In particular, one of the
main challenges is to tackle the problem in an unsupervised manner. In this
paper we propose an unsupervised framework for bitemporal heterogeneous change
detection based on the comparison of affinity matrices and image regression.
First, our method quantifies the similarity of affinity matrices computed from
co-located image patches in the two images. This is done to automatically
identify pixels that are likely to be unchanged. With the identified pixels as
pseudo-training data, we learn 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 process regression, support vector
regression, random forest regression, and a recently proposed kernel regression
method called homogeneous pixel transformation. To evaluate the potentials and
limitations of our framework, and also the benefits and disadvantages of each
regression method, we perform experiments on two real data sets. The results
indicate that the comparison of the affinity matrices can already be considered
a change detection method by itself. However, image regression is shown to
improve the results obtained by the previous step alone and produces accurate
change detection maps despite of the heterogeneity of the multitemporal input
data. Notably, the random forest regression approach excels by achieving
similar accuracy as the other methods, but with a significantly lower
computational cost and with fast and robust tuning of hyperparameters.
