Paper
13 September 2004 Anomalies detection in hyperspectral imagery using projection pursuit algorithm
Veronique Achard, Anthony Landrevie, Jean Claude Fort
Author Affiliations +
Proceedings Volume 5573, Image and Signal Processing for Remote Sensing X; (2004) https://doi.org/10.1117/12.567664
Event: Remote Sensing, 2004, Maspalomas, Canary Islands, Spain
Abstract
Hyperspectral imagery provides detailed spectral information on the observed scene which enhances detection possibility, in particular for subpixel targets. In this context, we have developed and compared several anomaly detection algorithms based on a projection pursuit approach. The projection pursuit is performed either on the ACP or on the MNF (Minimum Noise Fraction) components. Depending on the method, the best axes of the eigenvectors basis are directly selected, or a genetic algorithm is used in order to optimize the projections. Two projection index (PI) have been tested: the kurtosis and the skewness. These different approaches have been tested on Aviris and Hymap hyperspectral images, in which subpixel targets have been included by simulation. The proportion of target in pixels varies from 50% to 10% of the surface. The results are presented and discussed. The performance of our detection algorithm is very satisfactory for target surfaces until 10% of the pixel.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Veronique Achard, Anthony Landrevie, and Jean Claude Fort "Anomalies detection in hyperspectral imagery using projection pursuit algorithm", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (13 September 2004); https://doi.org/10.1117/12.567664
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Cited by 6 scholarly publications.
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KEYWORDS
Target detection

Principal component analysis

Detection and tracking algorithms

Reflectivity

Genetic algorithms

Hyperspectral target detection

Hyperspectral imaging

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