The access to many sources of satellite information is nowadays a reality. However, few methods allow to consider simultaneously data coming from different sensors, due to the differences in numbers of bands, spatial resolution and changes in the acquisition conditions. In this paper, we propose a methodology to align the data structures (also called manifolds) of two (or more) images and to exploit them simultaneously in a joint latent space. The method being invertible, it also have the interesting property to allow to project the image pixels from one sensor to another, thus allowing to synthesize the bands of one sensor using the pixels of the other through the projection learned. Experiments using QuickBird and World-View II images sho...
Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing. O...
International audienceThe use of several images of various modalities has been proved to be useful f...
In this paper, we study a family of semisupervised learning algorithms for "aligning" di...
The access to many sources of satellite information is nowadays a reality. However, few methods allo...
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing ...
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing ...
Manifold alignment has become very popular in recent literature. Aligning data distributions prior t...
The re-use of available labeled samples to classify newly acquired data is a hot topic in pattern an...
Remote sensing image classification exploiting multiple sensors is a very challenging problem: data ...
Aligning data distributions that underwent spectral distortions related to acquisition conditions is...
Aligning data distributions that underwent spectral distortions related to acquisition conditions is...
This paper presents a method for alignment of images acquired by sensors of different modalities (e....
Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. H...
In this paper, we propose a novel manifold alignment method by learning the underlying common manifo...
International audience<p>Alignment and parallelism are frequently found between objects in high reso...
Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing. O...
International audienceThe use of several images of various modalities has been proved to be useful f...
In this paper, we study a family of semisupervised learning algorithms for "aligning" di...
The access to many sources of satellite information is nowadays a reality. However, few methods allo...
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing ...
We introduce a method for manifold alignment of different modalities (or domains) of remote sensing ...
Manifold alignment has become very popular in recent literature. Aligning data distributions prior t...
The re-use of available labeled samples to classify newly acquired data is a hot topic in pattern an...
Remote sensing image classification exploiting multiple sensors is a very challenging problem: data ...
Aligning data distributions that underwent spectral distortions related to acquisition conditions is...
Aligning data distributions that underwent spectral distortions related to acquisition conditions is...
This paper presents a method for alignment of images acquired by sensors of different modalities (e....
Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. H...
In this paper, we propose a novel manifold alignment method by learning the underlying common manifo...
International audience<p>Alignment and parallelism are frequently found between objects in high reso...
Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing. O...
International audienceThe use of several images of various modalities has been proved to be useful f...
In this paper, we study a family of semisupervised learning algorithms for "aligning" di...