Spectral-Structural Collaborative Learning for Fine-Grained Hyperspectral Mineral Classification.

Yichun Qiu, Yanshuang Zhang, Shixian Cao, Wenyuan Wu, Shanjuan Xie

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yichun QiuSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.ORCID 0009-0005-1721-2290

Yanshuang ZhangCollege of Artificial Intelligence and E-Commerce, Zhejiang Gongshang University Hangzhou College of Commerce, Hangzhou 311121, China.

Shixian CaoSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.

Wenyuan WuSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.ORCID 0000-0001-8817-2920

Shanjuan XieSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.

Funding

National Natural Science Foundation of China No. 41402304National Natural Science Foundation of China No. 61701153Zhejiang Provincial Natural Science Foundation LQ13D020002

6 · The paper itself

Abstract

Fine-grained hyperspectral mineral classification remains challenging due to spectral homogeneity among minerals with different morphologies, severe spectral mixing from intergrowth, and high dimensionality. Existing methods rely on spectral separability assumptions, which become insufficient when spectral differences are subtle and spatial-structural ambiguity is high. To address these limitations, we propose S

Indexed as

collaborative attention paradigmdeep learningfine-grained hyperspectral mineral classificationhyperspectral image classification

Identifiers

PMID42515229

DOI10.3390/s26144345

PMCPMC13416926

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.