A Free & Open Source QGIS Plug-in for Advanced Hyperspectral Image Analysis

Contributors

Mr. Rosly Boy Lyngdoh, Dr. Anand S Sahadevan, Mr. Touseef Ahmad, Mr. Pradyuman Singh Rathore, Dr. Manoj Mishra , Dr. Praveen Kumar Gupta and Dr. Arundhati Misra

Hyperspectral Techniques Development Division

Advanced Microwave and Hyperspectral Techniques Development Group | EPSA

Space Applications Centre | Indian Space Research Organisation | Ahmadabad | Gujarat | India - 380015

About

Advanced Hyperspectral Data Analysis (AVHYAS) plugin is a Python-3 based QGIS-plugin designed to process and analyse hyperspectral (Hx) images. Starting with the version 1.0, AVHYAS serves as a free and open-source platform for sharing and distributing algorithms and methods among scientists and potential end-users. It is developed to guarantee full usage of future hyperspetcral missions of Indian Space Research Organisation (ISRO) and other space agencies and providing access to advanced algorithms for Hx data processing. The software is freely available and offers a range of tools and applications for the atmospheric correction of airborne AVIRIS-NG image, classical processing tools for Hx data as well as powerful machine learning and Deep Learning interfaces for the interdisciplinary users. This paper gives an overview of the AVHYAS plugin for users, explains typical workflows and use cases for making it a constantly used platform for hyperspectral remote sensing applications.


If you find AVHYAS useful in your research, please consider citing:

Rosly Boy Lyngdoh , Anand S Sahadevan , Touseef Ahmad , Pradyuman Singh Rathore , Manoj Mishra , Praveen Kumar Gupta, Arundhati Misra, "AVHYAS: A Free and Open Source QGIS Plug-in for Advanced Hyperspectral Image Analysis", IEEE International Conference on Emerging Techniques in Computational Intelligence – ICETCI-2021.

Unique features

AVHYAS_TECHNICAL_DOCUMENT_USER_MANUAL.pdf

Deep Learning for Hyperspectral Data Classification

Class Separability Analysis

Hx-Mx data Fusion

Hx Cloud Removal

Training Announcement: 11-12 February, 2021

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