21 October 2016 Use of multivariate analysis to minimize collecting of infrared images and classify detected objects
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Abstract
An infrared image contains spatial and radiative information about objects in a scene. Two challenges are to classify pixels in a cluttered environment and to detect partly obscured or buried objects like mines and IEDs. Infrared image sequences provide additional temporal information, which can be utilized for a more robust object detection and an improved classification of object pixels. A manual evaluation of multi-dimensional data is generally time consuming and inefficient and therefore various algorithms are used. By a principal component analysis (PCA) most of the information is retained in a new, reduced system with fewer dimensions. The principal component coefficients (loadings) are here used both for classifying detected object pixels and for reducing the number of images in the analysis by computing of score vectors. For the datasets studied, the number of required images can be reduced significantly without loss of detection and classification ability. This allows for a more sparse sampling and scanning of larger areas when using a UAV, for example.
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Thomas Svensson, Dietmar Letalick, "Use of multivariate analysis to minimize collecting of infrared images and classify detected objects", Proc. SPIE 9988, Electro-Optical Remote Sensing X, 99880M (21 October 2016); doi: 10.1117/12.2241821; https://doi.org/10.1117/12.2241821
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