LUPPINO L. T.

Change detection represents a major family of remote sensing image analysis techniques and plays a fundamental role in a variety of applications to environmental monitoring and disaster risk management. However, most change detection methods operate under the assumption that the multitemporal input data have been collected with the same (or...

Change detection (CD) 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 CD based on the comparison of affinity...

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...

Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels...

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...