Hyperspectral unmixing using L2,1 norm and total variation for material detection on earth's surface

Danan Arya Pradana, Icha Fatwasauri, Mia Rizkinia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hyperspectral imaging is often used to determine what components present in a scene of the earth's surface. Each pixel in a hyperspectral image may contain of a pure material or a mixture of multiple materials due to the limitation of spatial resolution. To determine the abundance of each component in a pixel, a process called hyperspectral unmixing was introduced. In hyperspectral unmixing, each pixel in an image is compared to a spectral library to determine material types and their proportion in the pixel. In this study, we construct an algorithm to optimize the hyperspectral unmixing problem using L2,1 norm and Total Variation regularization to reduce reconstruction error. Specifically, our research aims to improve the unmixing results by applying L2,1 norm to impose collaborative sparsity on all pixels in the image and adding Total Variation regularization to improve the smoothness of resulting image. Our experimental results with both synthetic and real hyperspectral data show improvements in terms of lower RMSE and higher SSIM than those of other methods.

Original languageEnglish
Title of host publication2019 16th International Conference on Quality in Research, QIR 2019 - International Symposium on Electrical and Computer Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728118987
DOIs
Publication statusPublished - Jul 2019
Event16th International Conference on Quality in Research, QIR 2019 - Padang, Indonesia
Duration: 22 Jul 201924 Jul 2019

Publication series

Name2019 16th International Conference on Quality in Research, QIR 2019 - International Symposium on Electrical and Computer Engineering

Conference

Conference16th International Conference on Quality in Research, QIR 2019
CountryIndonesia
CityPadang
Period22/07/1924/07/19

Keywords

  • Convex optimization
  • Hyperspectral unmixing
  • L norm
  • Total variation

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