Simulated annealing: theory and applications
Simulated annealing: theory and applications
Projection pursuit exploratory data analysis
Computational Statistics & Data Analysis
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hyperspectral imagery: clutter adaptation in anomaly detection
IEEE Transactions on Information Theory
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Genetic algorithms and particle swarm optimization for exploratory projection pursuit
Annals of Mathematics and Artificial Intelligence
Expert Systems: The Journal of Knowledge Engineering
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The main goal of this paper is to propose an innovative technique for anomaly detection in hyperspectral imageries. This technique allows anomalies to be identified whose signatures are spectrally distinct from their surroundings, without any a priori knowledge of the target spectral signature. It is based on an one-dimensional projection pursuit with the Legendre index as the measure of interest. The index optimization is performed with a simulated annealing over a simplex in order to bypass local optima which could be sub-optimal in certain cases. It is argued that the proposed technique could be considered as seeking a projection to depart from the normal distribution, and unfolding the outliers as a consequence. The algorithm is tested with AHS and HYDICE hyperspectral imageries, where the results show the benefits of the approach in detecting a great variety of objects whose spectral signatures have sufficient deviation from the background. The technique proves to be automatic in the sense that there is no need for parameter tuning, giving meaningful results in all cases. Even objects of sub-pixel size, which cannot be made out by the human naked eye in the original image, can be detected as anomalies. Furthermore, a comparison between the proposed approach and the popular RX technique is given. The former outperforms the latter demonstrating its ability to reduce the proportion of false alarms.