Aug 11, 2021 · We propose a new self-supervised training objective, Contrastive Sensor Fusion, which exploits coterminous data from multiple sources to learn useful ...

Code implementing Contrastive Sensor Fusion, an approach for unsupervised learning of multi-sensor representations targeted at remote sensing imagery. Check out ...

This work proposes a new self-supervised training objective, Contrastive Sensor Fusion, which exploits coterminous data from multiple sources to learn ...

In the application of machine learning to remote sensing, labeled data is often scarce or expensive, which impedes the training of powerful models like deep.

We propose a new self-supervised training objective, Contrastive Sensor Fusion, which exploits coterminous data from multiple sources to learn useful ...

Explore all code implementations available for Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach.

We propose a novel self-supervised representation learning method based on temporal prediction for remote sensing image CD.

Missing: Fusion | Show results with:Fusion

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks.

Sep 1, 2021 · The authors of the paper Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach, realised that this is a major ...