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Hyperspectral image vegetation change detection based on biochemical parameters inversion

  • School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

Change detection of remote sensing images is a technology that one can get the change information by observing images of the same place obtained at different times. Hyperspectral remote sensing images can record detailed spectral information and reflect subtle differences between target and background. Hyperspectral change detection methods focus on changes between the different categories of feature, without fully taking the changes within the single ground type into account. In this paper, a hyperspectral vegetation change detection method based on biochemical parameters inversion is proposed. The change can be extracted from the vegetation biochemical parameters image by analyzing leaf water content, lignin content and other biochemical parameters. Experiments are conducted on both airborne and ground-based observation data. It shows that the change detection method based on biochemical parameters inversion reaches a high detection rate of 87.5% with a low false detection rate, which demonstrates superiority of the change detection methodology we proposed compared to other traditional methods.

Original languageEnglish
Title of host publicationCommunications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems
EditorsQilian Liang, Min Jia, Jiasong Mu, Wei Wang, Xuhong Feng, Baoju Zhang
PublisherSpringer Verlag
Pages629-637
Number of pages9
ISBN (Print)9789811065705
DOIs
StatePublished - 2019
Externally publishedYes
Event6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 - Harbin, China
Duration: 14 Jul 201716 Jul 2017

Publication series

NameLecture Notes in Electrical Engineering
Volume463
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017
Country/TerritoryChina
CityHarbin
Period14/07/1716/07/17

Keywords

  • Change detection
  • Component analysis
  • Hyperspectral remote sensing image
  • Vegetation biochemical parameters
  • Vegetation index

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