A robust Evidential Fisher Discriminant for multi-temporal hyperspectral images classification

Hemissi, Selim; Farah, Imed Riadh; Ettabaa, Karim Saheb; Solaiman, Basel

doi:10.1109/igarss.2012.6351723

Cited by 2 publications

(1 citation statement)

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“…In fact, the theory of belief functions showed a major potential and a reliable framework for modeling uncertain and imprecise class information in related fields such as classification, unmixing and feature combination (Hemissi et al, 2012). Let now denote by Ω the set of classes and the learning dataset becomes :…”

Section: Proposed Approachmentioning

confidence: 99%

A Class-Outlier Approach for Environnemental Monitoring Using Uav Hyperspectral Images

Hemissi

Farah²

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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.

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Section: Proposed Approachmentioning

confidence: 99%