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Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Behnood Rasti, Alexandre Zouaoui, Julien Mairal +1
Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and inti…
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery
Jules Bourcier, Gohar Dashyan, Jocelyn Chanussot +1
The application of deep neural networks to remote sensing imagery is often constrained by the lack of ground-truth annotations. Adressing this issue requires models that generalize…
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing
Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti +2
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a natural fo…
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation
Na Liu, Wei Li, Yinjian Wang +3
The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy. Howe…
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
Danfeng Hong, Jing Yao, Deyu Meng +2
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perf…
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot +1
Hyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming…