HANSEN M. A.

Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by...

This paper proposes a new method for bitemporal change detection in heterogeneous remote sensing images. A modified canonical correlation analysis is used to align the code layers of two deep convolutional autoencoders, one for each image domain. It weights the input with a new affinity-based prior, which measures changes in pixel relations...

A new methodology for unsupervised heterogeneous change detection has recently been proposed, which combines deep neural networks for domain alignment and image-to-image regression with a comparison of domain-specific pixel affinities to reveal structural changes. In this paper we explain the underlying cross-domain dissimilarity measure and...