Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data

Ensemble classifiers for land cover mapping

This study presents experimental investigations on supervised ensemble classification for land cover classification. Despite the arrays of classifiers available in machine learning to create an ensemble, knowing and understanding the correct classifier to use for a particular dataset remains a major challenge. The ensemble method increases classification accuracy by consulting experts taking final decision in the classification process. This study generated various land cover maps, using image classification. This is to authenticate the number of classifiers that should be used for creating an ensemble. The study exploits feature selection techniques to create diversity in ensemble classification. Landsat imagery of Kampala (the capital of Uganda, East Africa), AVIRIS hyperspectral dataset of Indian pine of Indiana and Support Vector Machines were used to carry out the investigation. The research reveals that the superiority of different classification approaches employed depends on the datasets used. In addition, the pre-processing stage and the strategy used during the designing phase of each classifier is very essential. The results obtained from the experiments conducted showed that, there is no significant benefit in using many base classifiers for decision making in ensemble classification. The research outcome also reveals how to design better ensemble using feature selection approach for land cover mapping. The study also reports the experimental comparison of generalized support vector machines, random forests, C4.5, neural network and bagging classifiers for land cover classification of hyperspectral images. These classifiers are among the state-of-the-art supervised machine learning methods for solving complex pattern recognition problems. The pixel purity index was used to obtain the endmembers from the Indiana pine and Washington DC mall hyperspectral image datasets. Generalized reduced gradient optimization algorithm was used to estimate fractional abundance in the image dataset thereafter obtained numeric values for land cover classification. The fractional abundance of each pixel was obtained using the spectral signature values of the endmembers and pixel values of class labels. As the results of the experiments, the classifiers show promising results. Using Indiana pine and Washington DC mall hyperspectral datasets, experimental comparison of all the classifiers’ performances reveals that random forests outperforms the other classifiers and it is computational effective. The study makes a positive contribution to the problem of classifying land cover hyperspectral images by exploring the use of generalized reduced gradient method and five supervised classifiers. The accuracy comparison of these classifiers is valuable for decision makers to consider tradeoffs in method accuracy versus complexity. The results from the research has attracted nine publications which include, six international and one local conference papers, one published in Computing Research Repository (CoRR), one Journal paper submitted and one Springer book chapter, Abe et al., 2012 obtained a merit award based on the reviewer reports and the score reports of the conference committee members during the conference period

Analyse hiérarchique d'images multimodales

There is a growing interest in the development of adapted processing tools for multimodal images (several images acquired over the same scene with different characteristics). Allowing a more complete description of the scene, multimodal images are of interest in various image processing fields, but their optimal handling and exploitation raise several issues. This thesis extends hierarchical representations, a powerful tool for classical image analysis and processing, to multimodal images in order to better exploit the additional information brought by the multimodality and improve classical image processing techniques. %when applied to real applications. This thesis focuses on three different multimodalities frequently encountered in the remote sensing field. We first investigate the spectral-spatial information of hyperspectral images. Based on an adapted construction and processing of the hierarchical representation, we derive a segmentation which is optimal with respect to the spectral unmixing operation. We then focus on the temporal multimodality and sequences of hyperspectral images. Using the hierarchical representation of the frames in the sequence, we propose a new method to achieve object tracking and apply it to chemical gas plume tracking in thermal infrared hyperspectral video sequences. Finally, we study the sensorial multimodality, being images acquired with different sensors. Relying on the concept of braids of partitions, we propose a novel methodology of image segmentation, based on an energetic minimization framework.Il y a un intérêt grandissant pour le développement d’outils de traitements adaptés aux images multimodales (plusieurs images de la même scène acquises avec différentes caractéristiques). Permettant une représentation plus complète de la scène, ces images multimodales ont de l'intérêt dans plusieurs domaines du traitement d'images, mais les exploiter et les manipuler de manière optimale soulève plusieurs questions. Cette thèse étend les représentations hiérarchiques, outil puissant pour le traitement et l’analyse d’images classiques, aux images multimodales afin de mieux exploiter l’information additionnelle apportée par la multimodalité et améliorer les techniques classiques de traitement d’images. Cette thèse se concentre sur trois différentes multimodalités fréquemment rencontrées dans le domaine de la télédétection. Nous examinons premièrement l’information spectrale-spatiale des images hyperspectrales. Une construction et un traitement adaptés de la représentation hiérarchique nous permettent de produire une carte de segmentation de l'image optimale vis-à-vis de l'opération de démélange spectrale. Nous nous concentrons ensuite sur la multimodalité temporelle, traitant des séquences d’images hyperspectrales. En utilisant les représentations hiérarchiques des différentes images de la séquence, nous proposons une nouvelle méthode pour effectuer du suivi d’objet et l’appliquons au suivi de nuages de gaz chimique dans des séquences d’images hyperspectrales dans le domaine thermique infrarouge. Finalement, nous étudions la multimodalité sensorielle, c’est-à-dire les images acquises par différents capteurs. Nous appuyant sur le concept des tresses de partitions, nous proposons une nouvelle méthodologie de segmentation se basant sur un cadre de minimisation d’énergie

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Application of high resolution remote sensing to detect and map the pasture weed Paterson’s curse (Echium plantagineum) in Western Australia

This study investigated the utility of three types of remotely sensed data (field spectroscopy, airborne multispectral and satellite hyperspectral) for detecting and mapping Paterson’s curse (Echium plantagineum) in the Wheatbelt Region of Western Australia. Using different classification, statistical and quantitative validation approaches, the study found that spectral resolution and timing of image capture were the most important factors for discriminating Paterson’s curse and producing acceptable levels of mapping accuracy

Efficacy of morphological approach in the classification of urban land covers.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Understanding the often-heterogeneous land use land cover (LULC) in urban areas is critical for among others environmental monitoring, spatial planning and enforcement. Recently, several earth observation satellites have been developed with enhanced spatial resolution that provide for precise and detailed representation of image objects. This has generated new demand for enhanced processing capabilities. Thus, the need for techniques that incorporate spatial and spectral information in the analysis of urban LULC has drawn increasing attention. Enhanced spatial resolution comes with challenges for most pixel based classifiers. This include salt and pepper effects that arise from incapability of pixel based techniques in considering spatial or contextual information related to the pixel of interest during image analysis. These challenges have often contributed to the inaccuracy of heterogeneous LULC classification. Object based techniques on the other hand have been proposed to provide effective framework for incorporating spatial information in their analysis. However, challenges such as over/under segmentation and difficulty or non-robust statistical estimation hamper most object techniques in achieving optimum performance. Thus, to achieve optimum LULC classification, the full exploitation of both spectral-spatial information is essential. Hence, this study investigated the efficacy of Mathematical Morphological (MM) techniques referred to as morphological profiles (MP) in LULC classification of a heterogeneous urban landscape. The first objective of the study evaluated two MP techniques i.e. concatenation of morphological profiles (CMP) and multi-morphological profiles (MMP) in the classification of a heterogeneous urban LULC. Findings from this study indicated that both CMP and MMP provided higher accuracies in classifying a heterogeneous urban landscape. However, in evaluating their capability in preserving geometrical characteristics such as shape, theme, edge and positional similarity of image structures, CMP provided higher accuracies than MMP. This was attributed to the use of Principal Component Analysis (PCA) in the construction of MMP that resulted in the distorted edges of some of the image objects. However, in comparing the techniques in terms of the capability to discriminate image objects, MMP provided higher classification accuracies compared to CMP. This can be attributed to the former’s capability to exploit both spectral and spatial information from very high spatial resolution imagery. Hence in the second objective, MMP was adopted due to its ability to deal with dimensionality problem associated with CMP and its superior object discrimination capability. The findings indicated that MMP significantly enhanced ML and SVM classification accuracies. Specifically, the use of MMP as a feature vector for SVM and ML classification increased LULC distinction of objects with similar spectral signatures in a heterogeneous urban landscape. This is due to its capability to provide an effective framework for synthesis of spectral and spatial information. Overall the study demonstrated that morphological techniques provides robust novel image analysis techniques which can effectively be used for operational classification of a heterogeneous urban LULC

Development of a spectral unmixing procedure using a genetic algorithm and spectral shape

xvi, 85 leaves : ill. (chiefly col.) ; 29 cmSpectral unmixing produces spatial abundance maps of endmembers or ‘pure’ materials using sub-pixel scale decomposition. It is particularly well suited to extracting a greater portion of the rich information content in hyperspectral data in support of real-world issues such as mineral exploration, resource management, agriculture and food security, pollution detection, and climate change. However, illumination or shading effects, signature variability, and the noise are problematic. The Least Square (LS) based spectral unmixing technique such as Non-Negative Sum Less or Equal to One (NNSLO) depends on “shade” endmembers to deal with the amplitude errors. Furthermore, the LS-based method does not consider amplitude errors in abundance constraint calculations, thus, often leads to abundance errors. The Spectral Angle Constraint (SAC) reduces the amplitude errors, but the abundance errors remain because of using fully constrained condition. In this study, a Genetic Algorithm (GA) was adapted to resolve these issues using a series of iterative computations based on the Darwinian strategy of ‘survival of the fittest’ to improve the accuracy of abundance estimates. The developed GA uses a Spectral Angle Mapper (SAM) based fitness function to calculate abundances by satisfying a SAC-based weakly constrained condition. This was validated using two hyperspectral data sets: (i) a simulated hyperspectral dataset with embedded noise and illumination effects and (ii) AVIRIS data acquired over Cuprite, Nevada, USA. Results showed that the new GA-based unmixing method improved the abundance estimation accuracies and was less sensitive to illumination effects and noise compared to existing spectral unmixing methods, such as the SAC and NNSLO. In case of synthetic data, the GA increased the average index of agreement between true and estimated abundances by 19.83% and 30.10% compared to the SAC and the NNSLO, respectively. Furthermore, in case of real data, GA improved the overall accuracy by 43.1% and 9.4% compared to the SAC and NNSLO, respectively