Automatic target detection in infrared sequences through semantic labeling

Fabio Lavagetto

doi:10.1117/12.23563

Abstract

An innovative algorithm is presented for the automatic detection of spot targets in infrared terrain image sequences. The algorithm is designed to successfully perform target detection also in presence of deceitful hot background structures such as rocks and sand representing the hottest regions of the scene. The original contribution is represented by the thermal model adopted for the infrared scene and which drives the intraframe processing by iteratively applying three processing steps: i) thresholding ii) clustering iii) semantic labeling. The aim of the intraframe segmentation module is first to detect well contrasted targets and then verify the presence of less contrasted ones on the hypothesis that they all belong to the same formation. At each iteration within the intraframe analysis the thermal threshold is lowered and the hot part of the scene is processed to look for the presence of targets. By theans of different clustering procedures the hot part of the scene is partitioned into a set of connected objects each characterized by a feature vector consisting of area peak thermal value and centroid coordinates. The objects which correspond to the target model are marked as potential targets. If they have been already detected during the previous iterations without sensible changes in the descriptive parameters they are labeled as " confirmed" targets. If on the contrary the area has significantly increased they are labeled as " expanded" targets. If a

Citation Download Citation

Fabio Lavagetto, Fabio Lavagetto, "Automatic target detection in infrared sequences through semantic labeling", Proc. SPIE 1349, Applications of Digital Image Processing XIII, (1 November 1990); doi: 10.1117/12.23563; https://doi.org/10.1117/12.23563

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