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.
By Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz, Pierre Comon
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
arXiv:2609.28283v1 Announce Type: new
Abstract: Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and...
By Edgard Dabier, Christophe Kervazo, Pietro Gori, Florence Tupin
arXiv:2511. 07109v2 Announce Type: replace-cross Abstract: Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique for nonnegative data, with applications such as hyperspectral unmixing and topic modeling.
By Junjun Pan, Valentin Leplat, Michael Ng, Nicolas Gillis
Hyperspectral remote sensing provides dense spectral measurements that are indispensable for material-level Earth observation, yet the construction of a general-purpose hyperspectral foundation model...
arXiv:2610.27958v1 Announce Type: new
Abstract: Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation int...
By Fabian Conrad, Benjamin Berkels
HyperSAM is a promptable foundation model for hyperspectral remote sensing that integrates a data‑centric synthesis pipeline with a spectral adaptation architecture based on Segment Anything Model 3 (SAM3). The model generates full‑spectrum hyperspectral cubes from high‑resolution multispectral imagery using a physics‑informed abundance‑transfer generator, and employs SAM3‑derived pseudo‑masks for object‑centric supervision. With a frozen SAM3 RGB branch, a trainable hyperspectral encoder, ControlNet‑style feature injection, and a mixture‑of‑experts mask refiner, HyperSAM demonstrates strong generalization across diverse hyperspectral tasks such as classification, anomaly detection, change detection, target detection, and airborne oil‑spill mapping.
By Li Pang, Xinqiao Wu, Jing Yao, Pedram Ghamisi, Jun Zhou, Zhengchao Chen, Deyu Meng, Xiangyong Cao
AGSA-Net is a hyperspectral image classification framework that incorporates spectral unmixing priors through an abundance-guided self‑attention network. It first estimates physically meaningful subpixel abundance maps with non‑negativity and sum‑to‑one constraints, then uses these abundances to build an affinity prior that directs a spectral transformer to focus on class‑discriminative interactions. The transformer features are fused with compact abundance descriptors for final classification, and experiments on Indian Pines, Augsburg, and Berlin datasets show improved performance, especially in heterogeneous urban scenes.
By Nafisa Anjum, Satavisa Dey Borno, Ananna Saha, Mir Faiyaz Hossain, Sifat Momen, Nabeel Mohammed, Shafin Rahman
arXiv:2607.12782v2 Announce Type: replace
Abstract: Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective para...
By Mingzhen Xu, Haonan Guo, Di Wang, Yinghua Qu, Zhiliang Zhou, Lei Zhang, Huiwen Yao, Rui Zhao, Fengxiang Wang, Gang Wan, Bo Du, Liangpei Zhang
arXiv:2610.27874v1 Announce Type: new
Abstract: Hyperspectral Image (HSI) reconstruction has made substantial progress with the deep unfolding framework by decomposing the problem into a projection m...
By Shuqi Liu, Yingkai Zhang, Lin Gu, Tao Zhang, Ying Fu
arXiv:2602. 04795v3 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) is a popular data embedding technique.
By Olivier Vu Thanh, Nicolas Gillis
LESSViT introduces a sensor‑flexible Vision Transformer for hyperspectral imagery that uses a low‑rank structured attention mechanism (LESS Attention) to efficiently model joint spatial‑spectral interactions. By reducing the complexity from ≠O(N^2C^2) to ≠O(rNC) and adding channel‑agnostic patch embedding with wavelength‑aware positional encoding, it supports flexible spectral inputs. A hyperspectral masked autoencoder (HyperMAE) with decoupled spatial‑spectral masking further enables robust pretraining, and experiments on the SpectralEarth benchmark show improved robustness to spectral shifts while maintaining competitive in‑distribution performance.
By Haozhe Si, Yuxuan Wan, Yuqing Wang, Minh N. Do, Han Zhao