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Cette thèse s'inscrit dans le contexte de l'amélioration de la caractérisation de l'occupation du sol à partir d'observations de télédétection de natures très différentes : le radar polarimétrique et les images optiques multispectrales. Le radar polarimétrique permet la détermination de mécanismes de rétrodiffusion provenant de théorèmes de décomposition de l'information polarimétrique utiles à la classification des types d'occupation du sol. Cependant ces décompositions sont peu compréhensibles lorsque que plu- sieurs classes thématiques co-existent dans des proportions très variables au sein des cellules de résolution radar. Le problème est d'autant plus important que le speckle inhérent à l'imagerie radar nécessite l'estimation de ces paramètres sur des voisinages locaux. Nous nous interrogeons alors sur la capacité des données optiques multispectrales sensiblement plus résolues spatialement que le radar polarimétrique à améliorer la compréhension des mécanismes radar. Pour répondre à cette question, nous mettons en place une méthode de démélange des images radar polarimétrique par séparation thématique de sources. L'image optique peut être considérée comme un paramètre de réglage du radar fournissant une vue du mélange. L'idée générale est donc de commencer par un démélange thématique (décomposer l'information radar sur les types d'occupation du sol) avant de réaliser les décompositions polarimétriques (identifier des mécanismes de rétrodiffusion).Dans ce travail nous proposons d'utiliser un modèle linéaire et présentons un algorithme pour réaliser le démélange thématique. Nous déterminons ensuite la capacité de l'algorithme de démé- lange à reconstruire le signal radar observé. Enfin nous évaluons si l'information radar démélangée contient de l'information thématique pertinente. Cette évaluation est réalisée sur des données simulées que nous avons générées et sur des données Radarsat-2 complètement polarimétriques pour un cas d'application de mélange sol nu/forêt. Les résultats montrent que, malgré le speckle, la reconstruction est valable. Il est toujours possible d'estimer localement des bases thématiques permettant de décomposer l'information radar polarimétrique puis de reconstruire le signal observé. Cet algorithme de démélange permet aussi d'assimiler de l'information portée par les images optiques. L'évaluation de la pertinence thématique des bases de la décomposition est plus problématique. Les expériences sur des données simulées montrent que celles-ci représentent bien l'information thématique souhaitée, mais que cette bonne estimation est dépendante de la nature des types thématiques et de leurs proportions de mélange. Cette méthode nécessite donc des études complémentaires sur l'utilisation de méthodes d'estimation plus robustes aux statistiques des images radar. Son application à des images radar de longueur d'onde plus longue pourrait permettre, par exemple, une meilleure estimation du volume de végétation dans le contexte de forêts ouvertes / Land cover is a layer of information of significant interest for land management issues. In this context, combining remote sensing observations of different types is expected to produce more reliable results on land cover classification. The objective of this work is to explore the use of polarimetric radar images in association with co-registered higher resolution optical images. Extracting information from a polarimetric representation consists in decomposing it with target decomposition algorithms. Understanding these mechanisms is challenging as they are mixed inside the radar cell resolution but it is the key to producing a reliable land cover classification. The problem while using these target decomposition algorithms is that average physical parameters are obtained. As a result, each land cover type of a mixed pixel might not be well described by the average polarimetric parameters. The effect is all the more important as speckle affecting radar observations requires a local estimation of the polarimetric matrices. In this context, we chose to assess whether optical images can improve the understanding of radar images at the observation scale so as to retrieve more information. Spatial and spectral unmixing methods, traditionally designed for optical image fusion, were found to be an interesting framework. As a consequence, the idea of unmixing physical radar scattering mechanisms with the optical images is proposed. The original method developed is the decomposition of the polarimetric information, based on land cover type. This thematic decomposition is performed before applying usual target decomposition algorithms. A linear mixing model for radar images and an unmixing algorithm are proposed in this document. Having pointed out that the linear unmixing model is able to split off polarimetric information on a land cover type basis, the information contained in the unmixed matrices is evaluated. The assesment is carried out with generated simulated data and polarimetric radar images from the Radarsat-2 satellite. For this experiment, textit {Bare soil} and textit {Forested area} were considered for land cover types. It was found that despite speckle the reconstructed radar information after the unmixing is statically relevant with the observations. Moreover, the unmixing algorithm is capable of assimilating information from optical images. The question whether the unmixed radar images contain relevant thematic information is more challenging. Results on real and simulated data show that this capacity depends on the types of land cover considered and their respective proportions. Future work will be carried out to make the estimation step more robust to speckle and to test this unmixing algorithm on longer wavelength radar images. In this case, this method could be used to have a better estimation of vegetation biomass in the context of open forested areas

In recent years, the substantial increase in the number of spectral channels in optical remote sensing sensors allows more detailed spectroscopic analysis of objects on the Earth surface. Modern hyperspectral sensors are able to sample the sunlight reflected from a target on the ground with hundreds of adjacent narrow spectral channels. However, the increased spectral resolution comes at the price of a lower spatial resolution, e.g. the forthcoming German hyperspectral sensor Environmental Mapping and Analysis Program (EnMAP) which will have 244 spectral channels and a pixel size on ground as large as 30 m x 30 m. The main aim of this thesis is dealing with the problem of reduced spatial resolution in hyperspectral sensors. This is addressed first as an unmixing problem, i.e., extraction and quantification of the spectra of pure materials mixed in a single pixel, and second as a resolution enhancement problem based on fusion of multispectral and hyperspectral imagery. This thesis proposes novel methods for hyperspectral unmixing using sparse approximation techniques and external spectral dictionaries, which unlike traditional least squares-based methods, do not require pure material spectrum selection step and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. However, in previous works it has been shown that these methods suffer from some drawbacks, mainly from the intra dictionary coherence. To improve the performance of sparse spectral unmixing, the use of derivative transformation and a novel two step group unmixing algorithm are proposed. Additionally, the spatial homogeneity of abundance vectors by introducing a multi-look model for spectral unmixing is exploited. Based on the above findings, a new method for fusion of hyperspectral images with higher spatial resolution multispectral images is proposed. The algorithm exploits the spectral information of the hyperspectral image and the spatial information from the multispectral image by means of sparse spectral unmixing to form a new high spatial and spectral resolution hyperspectral image. The introduced method is robust when applied to highly mixed scenarios as it relies on external spectral dictionaries. Both the proposed sparse spectral unmixing algorithms as well as the resolution enhancement approach are evaluated quantitatively and qualitatively. Algorithms developed in this thesis are significantly faster and yield better or similar results to state-of-the-art methods.

In this dissertation, I design a processing approach, implement and test several solutions to combining spatial and spectral processing of multisource data. The measured spectral information is assumed to come from a multispectral or hyperspectral imaging system with low spatial resolution. Thematic content from a higher spatial resolution sensor is used to spatially localize different materials by their spectral signature. This approach results in both spectralunmixing and sharpening, a spatial-spectral fusion. The main real imagery example, fusion of polarimetric synthetic aperture radar (SAR) with hyperspectral imagery, poses a unique challenge due to the phenomenological differences between the sensors.Theoretical models for electro-optical image formation and scene reflectivity are shown to lead naturally to a set of pixel mixing equations. Several solutions for the spatial unmixing form of these equations are examined, based on the method of least squares. In particular, a method for introducing thematic content into the solution for spatial unmixing is defined using weighted least squares. Finally, and most significantly, a spatial-spectral fusion algorithm based on the theory of projection onto convex sets (POCS) is presented. Theoretical aspects of POCS are briefly discussed, showing how the use of constraints in the form of closed convex sets drives the solution. Then, constraints are derived that are intimately tied to the underlying theoretical models. Simulated imagery is used to characterize the different constraintcombinations that can be used in a POCS-based fusion algorithm.The fusion algorithms are applied to real imagery from two data sets, a Landsat ETM+ scene over Tucson, AZ and an AVIRIS/AirSAR scene over Tombstone, AZ. The results of the fusion are analyzed using scattergrams and correlation statistics. The POCS-based fusion algorithm is shown to produce a reasonable fusion of the AVIRIS/AirSAR data, with some sharpening of spatial-spectral features.

We have previously introduced a high throughput multiplexing computational spectral imaging device. The device measures scalar projections of pseudo-arbitrary spectral filters at each spatial pixel. This paper discusses simulation and initial experimental progress in performing computational spectral unmixing by taking advantage of the natural sparsity commonly found in the fractional abundances. The simulation results show a lower unmixing error compared to traditional spectral imaging devices. Initial experimental results demonstrate the ability to directly perform spectral unmixing with less error than multiplexing alone.

Wetlands are the most important and valuable ecosystems on Earth. They are called “kidneys of the Earth”. Vegetation change detection is necessary to understand the condition of a wetland and to support ecosystem sustainable management and utilization. It has been a great challenge to estimate vegetation (including bald cypress trees) coverage of the wetland because it is difficult to access directly. Satellite remote sensing technology can be one important feasible method to map and monitor changes of wetland forest vegetation and land cover over large areas. Remote sensing mapping techniques have been applied to detect and map vegetation changes in wetlands. To address spectral mixture issues associated with moderate resolution remote sensing images, many spectral mixture methods have been developed and applied to unmix the mixed pixels in order to accurately map endmembers (e.g., different land cover types and different materials within pixels) fractions or abundance. Of them, Mixture Tuned Matched Filtering (MTMF) is an advanced spectral unmixing method that has attracted many researchers to test it for mapping land cover types including mapping tree species with medium or coarse remote sensing image data. MTMF is a partial unmixing method that suppresses background noise and estimates the subpixel abundance of a single target material. In this study, to understand impacts of anthropogenic (e.g., urbanization) and natural forces/climate change on the bald cypress tree dynamic change, the bald cypress trees cover change in University of South Florida Forest Preserve Area was mapped and analysed by using MTMF tool and multitemporal Landsat imagery over 30 years from 1984 to 2015. To evaluate the MTMF’s performance, a tradition spectral unmixing method, Linear Spectral Unmixing (LSU), was also tested. The experimental results indicate that (1) the bald cypress tree cover percentage in the study area has generally increased during the 30 years from 1984 to 2015, but over the time period from 1994 to 2005, the bald cypress tree cover percentage reduced; (2) MTMF tool outperformed the LSU method in mapping the change of the bald cypress trees over the 30 years to demonstrate its powerful capability; and (3) there potentially exists an impact of human activities on the change of the bald cypress trees although a further quantitative analysis is needed in the future research.

Remote sensing has the potential to monitor spatial variation in water quality over large areas. While ocean colour work has developed analytical bio-optical water quality retrieval algorithms for medium spatial resolution platforms, remote sensing of lake water is often limited to high spatial resolution satellites such as Landsat, which have limited spectral resolution. This thesis presents the results of an investigation into satellite monitoring of lake water quality. The aim of this investigation was to ascertain the feasibility of estimating water quality and its spatial distribution using Landsat 7 ETM+ imagery combined with in situ data from Rotorua and Waikato lakes. For the comparatively deep Rotorua lakes, r² values of 0.91 (January 2002) and 0.83 (March 2002) were found between in situ chlorophyll (chl) a and the Band1/Band3 ratio. This technique proved useful for analysing the spatial distribution of phytoplankton, especially in lakes Rotoiti and Rotoehu. For the more bio-optically complex shallow lakes of the Waikato, a linear spectral unmixing (LSU) approach was investigated where the water surface reflectance spectrum is defined by the contribution from pure pixels or endmembers. The model estimates the percentage of the endmember within the pixel, which is then used in a final regression with in situ data to map water quality in all pixels. This approach was used to estimate the concentration of chl a (r² = 0.84). Total suspended solid (TSS) concentration was mapped using the traditional Band 3 regression with in situ data, which combined atmospherically corrected reflectance for both images into a single relationship (r² = 0.98). The time difference between in situ data collection and satellite data capture is a potential source of error. Other potential sources of error include sample location accuracy, the influence of dissolved organic matter, and masking of chl a signatures by high concentrations of TSS. The results from this investigation suggest that remote sensing of water quality provides meaningful and useful information with a range of applications and could provide information on temporal spatial variability in water quality.

Στην παρούσα εργασία γίνεται ανάλυση της διαδικασίας subpixel spectral unmixing. Η διαδικασία αυτή αρχικά αναπτύχθηκε για εφαρμογές δορυφορικών εικόνων, με την εξέλιξη της τεχνολογίας βρήκε σημαντικές εφαρμογές και σε άλλους τομείς όπως η ιατρική. Η τιμή ενός Pixel αντιστοιχεί στην τιμή του φάματος σε μία συγκεκριμένη ζώνη. Η τιμή αυτή συνήθως συνίσταται από ένα σύνολο τιμών του φάσματος που αντιστοιχούν σε χαρακτηριστικά (είδη επίγειας καλύψης). Με τη διαδικασία Pixel unmixing πραγματοποιείται ο διαχωρισμός των ειδών αυτών και ταυτόχρονα υπολογίζονται οι αναλογίες κάθε είδους σε κάθε pixel. Στην εργασία αυτή παρούσιάζονται: α) οι βασικές αρχές που διέπουν το ηλεκτρομαγνητικό φάσμα, β) οι διαδιασίες ταξινόμησης αντικειμένων, γ) βασικές αρχές τηλεπισκόπησης, δ) ανάπτυξη της πολυφασματικής ανάλυσης και τέλος ε)ανάπτυξη της γραμμικής διαδικασίας pixel unmixing και πειραματικές εφαρμογές σε matlab 6.5. / ----

The majority of the impact of climate and human induced changes on forest are related to climate variability and deforestation. Similarly, changes in forest phenology due to climate variability and deforestation has been recognized as being among the most important early indicators of the impact of environmental change on forest ecosystem functioning. Comprehensive data on baseline forest cover changes including deforestation is required to provide background information needed for governments to make decision on Reducing Emissions from Deforestation and Forest Degradation (REED). Despite the fact that Nigeria ranks among the countries with highest deforestation rates based on Food and Agricultural Organization estimates, only a few studies have aimed at mapping forest cover changes at country scales. However, recent attempts to map baseline forest cover and deforestation in Nigeria has been based on global scale remote sensing techniques which do not confirm with ground based observations at country level. The aim of this study is two-fold: firstly, baseline forest cover was estimated using an ‘adaptive’ remote sensing model that classified forest cover with high accuracies at country level for the savanna and rainforest zones. The first part of this study also compared the potentials of different MODIS data in detecting forest cover changes at regional (cluster level) scale. The second part of this study explores the trends and response of forest phenology to rainfall across four forest clusters from 2002 to 2012 using vegetation index data from the MODIS and rainfall data obtained from the TRMM.

As a result of its capacity for material discrimination, hyperspectral imaging has been utilized for applications ranging from mining to agriculture to planetary exploration. One of the most common methods of exploiting hyperspectral images is spectral unmixing, which is used to discriminate and locate the various types of materials that are present in the scene. When this processing is done without the aid of a reference library of material spectra, the problem is called blind or unsupervised spectral unmixing. Independent component analysis (ICA) is a blind source separation approach that operates by finding outputs, called independent components, that are statistically independent. ICA has been applied to the unsupervised spectral unmixing problem, producing intriguing, if somewhat unsatisfying results. This dissatisfaction stems from the fact that independent components are subject to a scale ambiguity which must be resolved before they can be used effectively in the context of the spectral unmixing problem. In this dissertation, ICA is explored as a spectral unmixing approach. Various processing steps that are common in many ICA algorithms are examined to assess their impact on spectral unmixing results. Synthetically-generated but physically-realistic data are used to allow the assessment to be quantitative rather than qualitative only. Additionally, two algorithms, class-based abundance rescaling (CBAR) and extended class-based abundance rescaling (CBAR-X), are introduced to enable accurate rescaling of independent components. Experimental results demonstrate the improved rescaling accuracy provided by the CBAR and CBAR-X algorithms, as well as the general viability of ICA as a spectral unmixing approach.