← Back to all news
arXiv Computer Vision By Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz, Pierre Comon

MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition

Read the original on arXiv Computer Vision →

The paper introduces MultiHU‑TD, an interpretable framework for multifeature hyperspectral unmixing that employs tensor decomposition and incorporates the abundance sum‑to‑one constraint via an ADMM algorithm. It extends previous models by adding mathematical morphology and neighborhood patch analysis, and provides detailed mathematical, physical, and graphical interpretations linked to the extended linear mixing model. Experiments on real hyperspectral images demonstrate the model’s interpretability and effectiveness, with code released on GitHub.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Sep 2

Agentic Multimodal Models for Environmental Hyperspectral Unmixing

The paper presents an agentic framework that uses a large vision‑language model to refine hyperspectral unmixing results from existing modular pipelines. By iteratively gathering spectral and spatial evidence through tools such as spectral‑library retrieval and abundance‑map visualization, the agent merges or discards endmembers and re‑estimates abundances. Experiments on HYDICE Urban, Jasper Ridge, and Stonewall Playa datasets show consistent improvements in endmember cardinality and overall decomposition quality across multiple pipelines, while remaining competitive with end‑to‑end methods.

By Micha{\l} Cholewa, Luca Ciampi, Nicola Messina, Przemys{\l}aw G{\l}omb, Giuseppe Amato
llmsagentsmultimodal
More like this →
arXiv Machine Learning
Aug 18

Convolution-Free Holistic Multivariance Decomposition Layer for Efficient Hyperspectral Image Classification Tensor Networks

arXiv:2608. 16241v1 Announce Type: cross Abstract: Feature extraction for hyperspectral image classification is conventionally addressed using rigid tensor decompositions that fail to capture complex spatio-spectral interdependencies, or heavily parameterized convolutional neural networks that are computationally expensive.

By S\"uha Tuna, \"Ulker Ba\c{s}ar
computer-visionbenchmarks
More like this →
Hugging Face Trending Papers
Jul 27

Structural Loss Metrics for Tensor Approximation via Matrix Low-Rank Approximation

Matricized low-rank approximation via SVD is a standard surrogate for tensor decompositions, but entry-wise reconstruction error fails to capture multiway geometric degradation. Under an orthogonal Tucker model, we characterize this degradation using two metrics: cross-mode Direction Loss, measuring geometric subspace deviation from rank truncation and noise rotation, and Interaction Loss, quantifying multilinear interaction distortion in the core tensor.

More like this →