The scientific applications and engineering aspects of multispectral and hyperspectral imaging systems have been studied extensively. The traditional geometric spectral imaging system model is specifically developed aiming at spectral sensors with spectrally non-overlapping bands. Spectral imaging systems with overlapping bands also exist. For example, the quantum-dot infrared photodetectors (QDIPs) for midwave- and longwave-infrared (IR) imaging systems exhibit highly overlapping spectral responses tunable through the bias voltages applied. This makes it possible to build spectrally tunable imaging system in IR range based on single QDIP. Furthermore, the QDIP based system can be operated as being adaptive to scenes. Other optical imaging systems like the human eye and some polarimetric sensing systems also have overlapping bands. To analyze such sensors, a functional analysis-based framework is provided in this dissertation. The framework starts from the mathematical description of the interaction between sensor and the radiation from scene reaching it. A geometric model of the spectral imaging process is provided based on the framework. The spectral response functions and the scene spectra are considered as vectors inside an 1-dimensional spectral space. The spectral imaging process is abstracted to represent a projection of scene spectrum onto sensor. The projected spectrum, which is the least-square error reconstruction of the scene vectors, contains the useful information for image processing. Spectral sensors with arbitrary spectral response functions are can be analyzed with this model. The framework leads directly to an image pre-processing algorithm to remove the data correlation between bands. Further discussion shows that this model can also serve the purpose of sensor evaluation, and thus facilitates comparison between different sensors. The spectral shapes and the Signal-to-Noise Ratios (SNR) of different bands are seen to influence the sensor's imaging ability in different manners, which are discussed in detail. With the newly defined SNR in spectral space, we can quantitatively characterize the photodetector noise of a spectral sensor with overlapping bands. The idea of adaptive imaging with QDIP based sensor is proposed and illustrated.

En fournissant en plus de l'information spatiale une mesure spectrale en fonction des longueurs d'ondes, l'imagerie hyperspectrale s'enorgueillie d'atteindre une précision bien plus importante que l'imagerie couleur. Grâce à cela, elle a été utilisée en contrôle qualité, inspection de matériaux,… Cependant, pour exploiter pleinement ce potentiel, il est important de traiter la donnée spectrale comme une mesure, d'où la nécessité de la métrologie, pour laquelle exactitude, incertitude et biais doivent être maitrisés à tous les niveaux de traitement.Face à cet objectif, nous avons choisi de développer une approche non-linéaire, basée sur la morphologie mathématique et de l'étendre au domaine spectral par le biais d'une relation d'ordre spectral basée sur les fonctions de distance. Une nouvelle fonction de distance spectrale et une nouvelle relation d'ordonnancement sont ainsi proposées. De plus, un nouvel outil d'analyse du basé sur les histogrammes de différences spectrales a été développé.Afin d'assurer la validité des opérateurs, une validation théorique rigoureuse et une évaluation métrologique ont été mises en œuvre à chaque étage de développement. Des protocoles d'évaluation de la qualité des traitements morphologiques sont proposés, exploitant des jeux de données artificielles pour la validation théorique, des ensembles de données dont certaines caractéristiques sont connues pour évaluer la robustesse et la stabilité et des jeux de données de cas réel pour prouver l'intérêt des approches en contexte applicatif. Les applications sont développées dans le contexte du patrimoine culturel pour l'analyse de peintures et pigments. / Providing not only spatial information but also spectral measure as a function of wavelength, hyperspectral imaging boasts a much greater gain in accuracy than the traditional color imaging. And for this capability, hyperspectral imaging has been employed for quality control, inspection of materials in various fields. However, to fully exploit this potential, it is important to process the spectral data as a measure. This induces the need of metrology where accuracy, uncertainty, and bias are managed at every level of processing.Aiming at developing a metrological image processing framework for spectral data, we select to develop a nonlinear approach using the mathematical morphology framework and extended it to the spectral domain by means of a distance-based ordering relation. A novel spectral distance function and spectral ordering relation are proposed, in addition of a new analysis tools based on spectral differences. To ensure the validity of the spectral mathematical morphology framework, rigorous theoretical validation and metrological assessment are carried out at each development stages. So, protocols for quality assessment of spectral image processing tools are developed. These protocols consist of artificial datasets to validate completely the theoretical requirements, datasets with known characteristics to assess the robustness and stability, and datasets from real cases to proof the usefulness of the framework on applicative context. The application tasks themselves are within the cultural heritage domain, where the target images come from pigments and paintings. / Hyperspektral avbildning muliggjør mye mer nøyaktige målinger enn tradisjonelle gråskala og fargebilder, gjennom både høy romlig og spektral oppløsning (funksjon av bølgelengde). På grunn av dette har hyperspektral avbildning blitt anvendt i økende grad ulike applikasjoner som kvalitetskontroll og inspeksjon av materialer. Men for å fullt ut utnytte sitt potensiale, er det viktig å være i stand til å behandle spektrale bildedata som målinger på en gyldig måte. Dette induserer behovet for metrologi, der nøyaktighet, usikkerhet og skjevhet blir adressert og kontrollert på alle nivå av bildebehandlingen.Med sikte på å utvikle et metrologisk rammeverk for spektral bildebehandling valgte vi en ikke-lineær metodikk basert på det etablerte matematisk morfologi-rammeverket. Vi har utvidet dette rammeverket til det spektrale domenet ved hjelp av en avstandsbasert sorteringsrelasjon. En ny spektral avstandsfunksjon og nye spektrale sorteringsrelasjoner ble foreslått, samt nye verktøy for spektral bildeanalyse basert på histogrammer av spektrale forskjeller.For å sikre gyldigheten av det nye spektrale rammeverket for matematisk morfologi, har vi utført en grundig teoretisk validering og metrologisk vurde-ring på hvert trinn i utviklingen. Dermed er og-så nye protokoller for kvalitetsvurdering av spektrale bildebehandlingsverktøy utviklet. Disse protokollene består av kunstige datasett for å validere de teoretiske måletekniske kravene, bildedatasett med kjente egenskaper for å vurdere robustheten og stabiliteten, og datasett fra reelle anvendelser for å bevise nytten av rammeverket i en anvendt sammenheng. De valgte anvendelsene er innenfor kulturminnefeltet, hvor de analyserte bildene er av pigmenter og malerier.