arXiv Computer Vision
Oct 1

Hyperspectral Image Models: Technical Report

The technical report introduces Hyperspectral Image Models, a modular framework that unifies 55 deep‑learning models across six paradigms for hyperspectral remote sensing. It standardizes tensor conventions, evaluation protocols, and dataset handling, integrating 24 benchmark scenes from various sensors and providing tools to avoid train‑test overlap. Experiments across 1,320 model‑scene combinations show that scene difficulty outweighs architecture, with no single paradigm dominating and small models achieving performance comparable to much larger ones.

By Tanishq Rachamalla, Aryan Das, Srishti Kaushik, Swalpa Kumar Roy
arXiv Computer Vision
4d ago

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models

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 Computer Vision
Oct 5

A PyTorch Library for Hyperspectral Image Models: Technical Report

The paper introduces a PyTorch library that unifies 55 hyperspectral image models across six deep‑learning paradigms, providing a common registry, automatic tensor adaptation, and standardized constructors. It includes 24 benchmark scenes from various sensors, with tools for data preprocessing, patch extraction, and rigorous train‑test partitioning to avoid overlap. Experiments on 1,320 model‑scene combinations show that scene difficulty outweighs architecture choice, with no single paradigm dominating and small models performing competitively with much larger ones.

By Tanishq Rachamalla, Aryan Das, Srishti Kaushik, Swalpa Kumar Roy