test_moissonnage_selune
type: Agent

Detection and classiﬁcation of multispectral infrared targets
type: Distribution
access URL: https://theses.hal.science/tel-00997684
format: HTML
issued: 2026-05-05T10:02:25.268396
modified: 2026-05-05T10:02:25.241325

Detection and classiﬁcation of multispectral infrared targets
type: Dataset
contact point: Organization
  fn: CCSD
keyword: anomaly-detection
keyword: markov-chain-monte-carlo-methods
keyword: multispectral-imagery
keyword: reconnaissance-de-forme
keyword: methodes-de-monte-carlo-par-chaines-de-markov
keyword: sequential-inference
keyword: modeles-a-prototype-deformable
keyword: deformable-template-models
keyword: infrared-signature
keyword: signature-infrarouge
keyword: detection-danomalies
keyword: apprentissage-sequentiel
keyword: imagerie-multispectrale
keyword: algorithmes-expectation-maximization
keyword: theses
keyword: spiotherengineering-sciences-physicsother
keyword: infoeu-reposemanticsdoctoralthesis
keyword: shape-recognition
keyword: mathmath-gmmathematics-mathgeneral-mathematics-mathgm
keyword: expectation-maximization-algorithm
landing page: https://theses.hal.science/tel-00997684
description: Surveillance systems should be able to detect potential threats far ahead in order to put forward a defence strategy. In this context, detection and recognition methods making use of multispectral infrared images should cope with low resolution signals and handle both spectral and spatial variability of the targets. We introduce in this PhD thesis a novel statistical methodology to perform aircraft detection and classiﬁcation which take into account these constraints. We ﬁrst propose an anomaly detection method designed for multispectral images, which combines a spectral likelihood measure and a level set study of the image Mahalanobis transform. This technique allows to identify images which feature an anomaly without any prior knowledge on the target. In a second time, these images are used as realizations of a statistical model in which the observations are described as random spectral and spatial deformation of prototype shapes. The model inference, and in particular the prototype shape estimation, is achieved through a novel unsupervised sequential learning algorithm designed for missing data models. This model allows to propose a classiﬁcation algorithm based on maximum a posteriori probability Promising results in detection as well as in classiﬁcation, justify the growing interest surrounding the development of multispectral imaging devices. These methods have also allowed us to identify the optimal infrared spectral band regroupments regarding the low resolution aircraft IRS detection and classiﬁcation
identifier: NNT: 2014TELE0007
issued: 2026-05-05T10:02:25.262876
language: fr
modified: 2026-05-05T10:02:25.262882