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Found 7 papers, 2 papers with code
Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images
Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate and habitability.
Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy
According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change.
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library.
On Clustering and Embedding Mixture Manifolds using a Low Rank Neighborhood Approach
Manifold clustering and embedding techniques appear to be an ideal tool for this task, but the present state-of-the-art algorithms perform poorly for hyperspectral data, particularly in the embedding task.
Inverting Variational Autoencoders for Improved Generative Accuracy
Recent advances in semi-supervised learning with deep generative models have shown promise in generalizing from small labeled datasets ($\mathbf{x},\mathbf{y}$) to large unlabeled ones ($\mathbf{x}$).
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene.
Perfect Recovery Conditions For Non-Negative Sparse Modeling
Sparse modeling has been widely and successfully used in many applications such as computer vision, machine learning, and pattern recognition.
