AUTOMATED TARGET RECOGNITION IN MULTI-SPECTRAL AND HIPER-SPECTRAL IMAGES, BASED ON MODEL, IN EIGENSPACES AND ON KLT - KARHUNEN-LOÈVE TRANSFORM
DOI: 10.5769/T2003001 and http://dx.doi.org/10.5769/T2003001
Author: Paulo Quintiliano da Silva
Advisor: Antonio Nuno de Castro Santa Rosa
University: University of Brasilia (UnB), Brazil
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In this Doctorate Thesis an automated target recognition approach was proposed using multispectral or hyperspectral images, based on model, on eigenspaces and on KLT- Karhunen-Loève Transform. For this, the ATR - Automated Target Recognition concepts are adapted to the Earth Sciences reality and to the characteristics of its targets, in the way of making possible the recognition of these targets in multispectral or hyperspectral images. The proposed approach uses KLT for the dimensionality reduction of the data.
In order to process the multispectral or hyperspectral images, the pixels are treated as if they were column-vectors, with so many lines as many spectral bands of the worked images have. In this way, these multispectral or hyperspectral images are stored in big vectors, and all of their pixels are represented in the form of two-dimensional images, in the way it is possible to use KLT.
The targets are represented by models in the eigenvalues and eigenvectors domain (i.e., eigenspace), obtained after the application of the KLT. These models are vectors built from eigenvectors with the biggest eigenvalues, with quantity of elements determined by the threshold applied in the eigenvalues cutting. In this way, both the standards used to the model training for each one of the classes, and the standards of the new targets submitted for recognition have their models, constituted by a vector with the descriptives obtained in eigenspace, working in the eigenvectors domain.
In the training time, using the supervised modality, based on the samples collected from all the worked classes are calculated and built the models of all the classes, already in the eigenvalues domain. In the recognition time, the new targets models are calculated and built. So, these models are compared with the classes models, by means of the Euclidean and Mahalanobis distances. Thus, these distances are calculated between the model of the submitted target for recognition and the models of all the worked classes. If the distance between the new target and the class "i" is the smaller, and if such distance is inside the threshold applied, then there was the recognition of the new target as belonging to class "i".
In order to demonstrate the proposed model operation, It was developed some target detection and image classification applications. Based on the obtained results, these applications drawn some maps with the classifications done and with the detection of the worked targets.
In the calculation of the thresholds, It was proposed the utilization of a Factor Q, which enables the opening or the closing of the thresholds, in the way of adjusting and controlling the indices of false-positive and of false-negative of the obtained results, allowing the adaptation of the approach to the specific needs of any applications.
Automated target recognition, classification, KLT, multispectral and hyperspectral images, thresholds.