CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY

A Self-supervised Approach for Fully Automated Urban Land Cover Classification of High-resolution Satellite Imagery

Commercially available high-resolution satellite imagery from sensors such as IKONOS and QuickBird are important data sources for a variety of urban area applications including infrastructure feature extraction and land cover mapping. Land cover maps from medium and high-resolution imagery are typically generated through supervised spectral classification of multispectral imagery. Supervised cl...

Time-Contrastive Networks: Self-Supervised Learning from Video

We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that ca...

Fusion-Reflection Self-Supervised Learning

By analyzing learning from the perspective of knowledge acquisition , a number of common limitations are overcome. Modeling efficacy is proposed as an empirical measure of knowledge, providing a concrete, mathematical means of “acquiring knowledge” via gradient ascent. A specific network architecture is described, a hierarchical analog of node-labeled Hidden Markov Models, and its evaluation an...

Information Fusion for Supervised Classiication in a Satellite Image

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low...