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Titre: Comment utiliser les histogrammes d’une image numérique

Auteurs: Camille Salou

Ecole: Ecole Nationale des Sciences Géographiques

Résumé: Dans une image, tout n’est pas toujours analysable, voire visible, d’un simple coup d’œil, et l’information contenue est rarement quantifiable de manière immédiate. Alors, comment saisir au mieux, de façon qualitative et quantitative, l’information contenue dans les différents pixels de l’image, sachant qu’un seul pixel synthétise parfois plusieurs informations différentes ? En effet, certains satellites permettent d’obtenir des images prises simultanément dans différents « canaux » (= bandes spectrales) : ces images qui apportent une information plus diversifiée sont appelées multispectrales. Par exemple, une image multispectrale d’un des satellites SPOT (1, 2 ou3) se décompose classiquement en 3 images superposées, acquises dans différents canaux.

Une simple image couleur peut également être considérée comme multispectrale puisqu’elle est codée sur les trois plans couleurs primaires : le rouge, le vert et le bleu. Dans de nombreux domaines, tels que l’environnement, l’urbanisme et la médecine notamment, l’image brute n’a aucun intérêt, son analyse est simplement inenvisageable dans cet état : il est nécessaire d’en extraire les régions clefs pour la rendre réellement utile, de la simplifier, de synthétiser l’information contenue dans les différents pixels… C’est pourquoi savoir repérer de façon plus ou moins automatique les zones homogènes de l’image se révèle fondamental. De nombreuses méthodes ont été mises au point dans ce but : beaucoup s’appuient sur l’analyse et l’utilisation des histogrammes de l’image. Parmi toutes ces méthodes, aucune n’est parfaite, aucune n’est universellement meilleure que les autres. Alors, comment choisir, dans une situation précise, la méthode la plus adaptée ?

Découvrons d’abord comment analyser une image à travers ses histogrammes, puis étudions les différentes façons de segmenter, et même plutôt de classer cette image en zones homogènes, grâce à notre connaissance de l’histogramme et aux modifications qu’on peut lui faire subir. Nous traiterons premièrement de la méthode la plus rudimentaire, le seuillage, puis nous aborderons des techniques de classification plus élaborées, en séparant les méthodes automatisées de celles nécessitant l’intervention d’un spécialiste.

Extrait du sommaire:

Introduction. 3
Chapitre I. Image et Histogramme. 5
Partie A. Lien entre image et histogramme. 5
Partie B. Différents types d’histogrammes. 6
Partie C. Analyse de l’histogramme d’une image numérique. 7
Chapitre II. Méthode simplifiée pour image en niveaux de gris :
Seuillage. 10
Partie A. Principe du seuillage. 10
Partie B. Exemples. 14
Chapitre III. Méthodes de classification. 15
Généralités. 15
Partie A. Méthodes de classification non supervisée. 17
Introduction. 17
1. La Classification Ascendante Hiérarchique. 17
2. La méthode des centres mobiles. 20
Partie B. Méthodes de classification supervisée. 21
Introduction. 21
1. La classification hypercube. 22
2. La classification par maximum de vraisemblance. 23
Contexte. 25
Table des illustrations. 26

Formation-Traitement d'image-cours 5

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