arXiv (All)
open access
Agentic Multimodal Models for Environmental Hyperspectral Unmixing
Hyperspectral unmixing is a key task in remote sensing that aims to decompose mixed pixels in hyperspectral images into their constituent material signatures, or endmembers, and their fractional abundances. Conventional modular approaches estimate the scene composition through successive model-order
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification#1123901
- A robust spectral-temporal index for cross-regional mapping of plastic-mulched farmland using decametric-resolution images#261222
- Detecting Landslides Beyond Hydrology: Spectral Indices and Geochemical Controls#686965
- Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery#173310
- Threshold‐Based Approach for Disaster Mapping Using Long‐Term Multispectral Image Series#358814
Record · ID 821241
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive