Superpixel guided weighted low-rank representation for nonlinear hyperspectral unmixing-Reference-Cited by-同舟云学术

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Superpixel guided weighted low-rank representation for nonlinear hyperspectral unmixing

Published:2023-12-10 Issue: Volume: Page:1-4
ISSN:
Container-title:2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)
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Author:

Ahmad Touseef¹,Sahadevan Anand S¹,Raha Soumyendu²,Lyngdoh Rosly B³,Putrevu Deepak¹

Affiliation:

1. Space Applications Centre (SAC) Indian Space Research Organization,Ahmedabad,Gujarat,India,380015

2. Indian Institute of Science,Dept. of Computational and Data Sciences,Bangalore,Karnataka,India,560012

3. North Eastern Space Applications Centre (NESAC),Department of Space,Umiam,Meghalaya,India,793103

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10490313/10490314/10490383.pdf?arnumber=10490383

Reference17 articles.

1. An Overview on Linear Unmixing of Hyperspectral Data

2. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

3. A Review of Nonlinear Hyperspectral Unmixing Methods

4. Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated‐forest hyperspectral data

5. Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model

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