World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

A Class-outlier Approach for Environnemental Monitoring Using Uav Hyperspectral Images : Volume Xl-7/W3, Issue 1 (30/04/2015)

By Hemissi, S.

Click here to view

Book Id: WPLBN0004015843
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: A Class-outlier Approach for Environnemental Monitoring Using Uav Hyperspectral Images : Volume Xl-7/W3, Issue 1 (30/04/2015)  
Author: Hemissi, S.
Volume: Vol. XL-7/W3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Publication Date:
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Hemissi, S., & Farah, I. R. (2015). A Class-outlier Approach for Environnemental Monitoring Using Uav Hyperspectral Images : Volume Xl-7/W3, Issue 1 (30/04/2015). Retrieved from

Description: Faculty of Applied Medical Sciences in Turbah, Taif University, KSA, RIADI Laboratory, University of Manouba, Campus universitaire de la Manouba, Tunisia. In several remote sensing applications, detecting exceptional/irregular regions (i.e, pixels) with respect to the whole dataset homogeneity is regarded as a very interested issue. Currently, this is limited to the pre-processing step aiming to eliminate the cloud or noisy pixels. In this paper, we propose to extend the coverage area and to tackle this issue by regarding the irregular/exceptional pixels as outliers. The main purpose is the adaptation of the class outlier mining concept in order to find abnormal and irregular pixels in hyperspectral images. This should be done taking into account the class labels and the relative uncertainty of collected data. To reach this goal, the Class Outliers: DistanceBased (CODB) algorithm is enhanced to take into account the multivariate high-dimensional data and the concomitant partially available knowledge of our data. This is mainly done by using belief theory and a learnable task-specific similarity measure. To validate our approach, we apply it for vegetation inspection and normality monitoring. For experimental purposes, the Airborne Prism Experiment (APEX) data, set acquired during an APEX flight campaign in June 2011, was used. Moreover, a collection of simulated hyperspectral images and spectral indices, providing a quantitative indicator of vegetation health, were generated for this purpose. The encouraging obtained results can be used to monitor areas where vegetation may be stressed, as a proxy to detect potential drought.



Click To View

Additional Books

  • Morphological Filling of Digital Elevati... (by )
  • Modeling an Application Domain Extension... (by )
  • Accurate Matching and Reconstruction of ... (by )
  • Wildlife Multispecies Remote Sensing Usi... (by )
  • Determining Pull – Out Deformations of B... (by )
  • Three-dimensional Data and the Recording... (by )
  • Mav-based Real-time Localization of Terr... (by )
  • Albedo Pattern Recognition and Time-seri... (by )
  • Land Cover Information Extraction Using ... (by )
  • Wcdrr and the Ceos Activities on Disater... (by )
  • Trabasa – Traditional Architecture Recor... (by )
  • Considering Internal Space Layout as a M... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.