Face Recognition via Ensemble Sift Matching of Uncorrelated Hyperspectral Bands and Spectral PCTS

Face recognition is not a new area of study, but facial recognition using through hyperspectral images is a somewhat new concept which is still in its infancy. Although the conventional method of face recognition using Red-Green-Blue (RGB) or grayscale images has been advanced over the last twenty years, these methods are still shown to have weak performance whenever there are variations or changes in lighting, pose, or temporal aspect of the subjects. A hyperspectral representation of an image captures more information that is available within a scene than a RGB image therefore it is beneficial to study the performance of face recognition using a hyperspectral representation of the subjects\u27 faces. We studied the results of a variety of methods for performing face recognition using the Scale Invariant Transformation Feature (SIFT) algorithm as a matching function on uncorrelated spectral bands, principal component representation of the spectral bands, and the ensemble decision of the two. We conclude that there is no dominating method in the scope of our research; however, we do obtain three methods with leading performances despite some trade-off between performance at lower ranks and performance at higher ranks...that outperform the results obtained from a previous study which only considered a SIFT application on a single hyperspectral band which also performs very well under temporal variation

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Hyperspectral-Augmented Target Tracking

With the global war on terrorism, the nature of military warfare has changed significantly. The United States Air Force is at the forefront of research and development in the field of intelligence, surveillance, and reconnaissance that provides American forces on the ground and in the air with the capability to seek, monitor, and destroy mobile terrorist targets in hostile territory. One such capability recognizes and persistently tracks multiple moving vehicles in complex, highly ambiguous urban environments. The thesis investigates the feasibility of augmenting a multiple-target tracking system with hyperspectral imagery. The research effort evaluates hyperspectral data classification using fuzzy c-means and the self-organizing map clustering algorithms for remote identification of moving vehicles. Results demonstrate a resounding 29.33% gain in performance from the baseline kinematic-only tracking to the hyperspectral-augmented tracking. Through a novel methodology, the hyperspectral observations are integrated in the MTT paradigm. Furthermore, several novel ideas are developed and implemented—spectral gating of hyperspectral observations, a cost function for hyperspectral observation-to-track association, and a self-organizing map filtering method. It appears that relatively little work in the target tracking and hyperspectral image classification literature exists that addresses these areas. Finally, two hyperspectral sensor modes are evaluated—Pushbroom and Region-of-Interest. Both modes are based on realistic technologies, and investigating their performance is the goal of performance-driven sensing. Performance comparison of the two modes can drive future design of hyperspectral sensors

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

The Ship Detection Using Airborne and In-situ Measurements Based on Hyperspectral Remote Sensing

International audienceMaritime accidents around the Korean Peninsula are increasing, and the ship detection research using remote sensing data is consequently becoming increasingly important. This study presented a new ship detection algorithm using hyperspectral images that provide the spectral information of several hundred channels in the ship detection field, which depends on high resolution optical imagery. We applied a spectral matching algorithm between the reflection spectrum of the ship deck obtained from two field observations and the ship and seawater spectrum of the hyperspectral sensor of an airborne visible/infrared imaging spectrometer. A total of five detection algorithms were used, namely spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), spectral angle mapper (SAM), and spectral information divergence (SID). SDS showed an error in the detection of seawater inside the ship, and SAM showed a clear classification result with a difference between ship and seawater of approximately 1.8 times. Additionally, the present study classified the vessels included in hyperspectral images by presenting the adaptive thresholds of each technique. As a result, SAM and SID showed superior ship detection abilities compared to those of other detection algorithms

Color in scientific visualization: Perception and image-based data display

Visualization is the transformation of information into a visual display that enhances users understanding and interpretation of the data. This thesis project has investigated the use of color and human vision modeling for visualization of image-based scientific data. Two preliminary psychophysical experiments were first conducted on uniform color patches to analyze the perception and understanding of different color attributes, which provided psychophysical evidence and guidance for the choice of color space/attributes for color encoding. Perceptual color scales were then designed for univariate and bivariate image data display and their effectiveness was evaluated through three psychophysical experiments. Some general guidelines were derived for effective color scales design. Extending to high-dimensional data, two visualization techniques were developed for hyperspectral imagery. The first approach takes advantage of the underlying relationships between PCA/ICA of hyperspectral images and the human opponent color model, and maps the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCbCr, and YUV. The gray world assumption was adopted to automatically set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes. The second approach combines a true color image and a PCA image based on a biologically inspired visual attention model that simulates the center-surround structure of visual receptive fields as the difference between fine and coarse scales. The model was extended to take into account human contrast sensitivity and include high-level information such as the second order statistical structure in the form of local variance map, in addition to low-level features such as color, luminance, and orientation. It generates a topographic saliency map for both the true color image and the PCA image, a difference map is then derived and used as a mask to select interesting locations where the PCA image has more salient features than available in the visible bands. The resulting representations preserve consistent natural appearance of the scene, while the selected attentional locations may be analyzed by more advanced algorithms

Identification of urban surface materials using high-resolution hyperspectral aerial imagery

La connaissance des matériaux de surface est essentielle pour l’aménagement et la gestion des villes. Avec les avancées en télédétection, particulièrement en imagerie de haute résolution spatiale et spectrale, l’identification et la cartographie détaillée des matériaux de surface en milieu urbain sont maintenant envisageables. Les signatures spectrales décrivent les interactions entre les objets au sol et le rayonnement solaire, et elles sont supposées uniques pour chaque type de matériau de surface. Dans ce projet de recherche nous avons utilisé des images hyperspectrales aériennes du capteur CASI, avec une résolution de 1 m2 et 96 bandes contigües entre 380nm et 1040nm. Ces images couvrant l’île de Montréal (QC, Canada), acquises en 2016, ont été analysées pour identifier les matériaux de surfaces. Pour atteindre ces objectifs, notre méthode d’analyse est fondée sur la comparaison des signatures spectrales d’un pixel quelconque à celles des objets typiques contenues dans des bibliothèques spectrales (matériaux inertes et végétation). Pour mesurer la correspondance entre la signature spectrale d’un objet et la signature spectrale de référence nous avons utilisé deux métriques. La première métrique tient compte de la forme d’une signature spectrale et la seconde, de la différence des valeurs de réflectance entre la signature spectrale observée et celle de référence. Un classificateur flou utilisant ces deux métriques est alors appliqué afin de reconnaître le type de matériau de surface sur la base du pixel. Des signatures spectrales typiques ont été extraites des deux librairies spectrales (ASTER et HYPERCUBE). Des signatures spectrales des objets typiques à Montréal mesurées sur le terrain (spectroradiomètre ASD) ont été aussi utilisées comme références. Trois grandes catégories de matériaux ont été identifiées dans les images pour faciliter la comparaison entre les classifications par source de références spectrales : l’asphalte, le béton et la végétation. La classification utilisant ASTER comme bibliothèque de référence a eu le plus grand taux de réussite avec 92%, suivi par ASD à 88% et finalement HYPERCUBE avec 80%. Nous 5 n’avons pas trouvé de différences significatives entre les trois résultats, ce qui indique que la classification est indépendante de la source des signatures spectrales de référence.Knowledge of surface cover materials is crucial for urban planning and management. With advances in remote sensing, especially in high spatial and spectral resolution imagery, the identification and detailed mapping of surface materials in urban areas based on spectral signatures are now feasible. Spectral signatures describe the interactions between ground objects and solar radiation and are assumed unique for each type of material. In this research, we use airborne CASI images with 1 m2 spatial resolution, with 96 contiguous bands in a spectral range between 367 nm and 1044 nm. These images covering the island of Montreal (Quebec, Canada), obtained in 2016, were analyzed to identify urban surface materials. The objectives of the project were first to find a correspondence between the physical and chemical characteristic of typical surface materials, present in the Montreal scenes, and the spectral signatures within the images. Second, to develop a sound methodology for identifying these surface materials in urban landscapes. To reach these objectives, our method of analysis is based on a comparison of pixel spectral signatures to those contained in a reference spectral library that describe typical surface covering materials (inert materials and vegetation). Two metrics were used in order to measure the correspondence of pixel spectral signatures and reference spectral signature. The first metric considers the shape of a spectral signature and the second the difference of reflectance values between the observed and reference spectral signature. A fuzzy classifier using these two metrics is then applied to recognize the type of material on a pixel basis. Typical spectral signatures were extracted from two spectral libraries (ASTER and HYPERCUBE). Spectral signatures of typical objects in Montreal measured on the ground (ASD spectroradiometer) were also used as reference spectra. Three general types of surface materials (asphalt, concrete, and vegetation) were used to ease the comparison between classifications using these spectral libraries. The classification using ASTER as a reference library had the highest success rate reaching 92%, followed by the field spectra at 88%, and finally with HYPERCUBE at 80%. There were no significant differences in the classification results indicating that the methodology works independently of the source of reference spectral signatures

Matched filter stochastic background characterization for hyperspectral target detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters, which may be derived in many different scientific fields, can be used to locate spectral targets by modeling scene background as either structured geometric) with a set of endmembers (basis vectors) or as unstructured stochastic) with a covariance matrix. In unstructured background research, various methods of calculating the background covariance matrix have been developed, each involving either the removal of target signatures from the background model or the segmenting of image data into spatial or spectral subsets. The objective of these methods is to derive a background which matches the source of mixture interference for the detection of sub pixel targets, or matches the source of false alarms in the scene for the detection of fully resolved targets. In addition, these techniques increase the multivariate normality of the data from which the background is characterized, thus increasing adherence to the normality assumption inherent in the matched filter and ultimately improving target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This thesis will establish a strong theoretical foundation, describing the necessary preprocessing of hyperspectral imagery, deriving the spectral matched filter, and capturing current methods of unstructured background characterization. The extensive experimentation will allow for a comparative evaluation of several current unstructured background characterization methods as well as some new methods which improve stochastic modeling of the background. The results will show that consistent improvements over the scene-wide statistics can be achieved through spatial or spectral subsetting, and analysis of the results provides insight into the tradespaces of matching the interference, background multivariate normality and target exclusion for these techniques

New algorithms for the analysis of live-cell images acquired in phase contrast microscopy

La détection et la caractérisation automatisée des cellules constituent un enjeu important dans de nombreux domaines de recherche tels que la cicatrisation, le développement de l'embryon et des cellules souches, l’immunologie, l’oncologie, l'ingénierie tissulaire et la découverte de nouveaux médicaments. Étudier le comportement cellulaire in vitro par imagerie des cellules vivantes et par le criblage à haut débit implique des milliers d'images et de vastes quantités de données. Des outils d'analyse automatisés reposant sur la vision numérique et les méthodes non-intrusives telles que la microscopie à contraste de phase (PCM) sont nécessaires. Comme les images PCM sont difficiles à analyser en raison du halo lumineux entourant les cellules et de la difficulté à distinguer les cellules individuelles, le but de ce projet était de développer des algorithmes de traitement d'image PCM dans Matlab® afin d’en tirer de l’information reliée à la morphologie cellulaire de manière automatisée. Pour développer ces algorithmes, des séries d’images de myoblastes acquises en PCM ont été générées, en faisant croître les cellules dans un milieu avec sérum bovin (SSM) ou dans un milieu sans sérum (SFM) sur plusieurs passages. La surface recouverte par les cellules a été estimée en utilisant un filtre de plage de valeurs, un seuil et une taille minimale de coupe afin d'examiner la cinétique de croissance cellulaire. Les résultats ont montré que les cellules avaient des taux de croissance similaires pour les deux milieux de culture, mais que celui-ci diminue de façon linéaire avec le nombre de passages. La méthode de transformée par ondelette continue combinée à l’analyse d'image multivariée (UWT-MIA) a été élaborée afin d’estimer la distribution de caractéristiques morphologiques des cellules (axe majeur, axe mineur, orientation et rondeur). Une analyse multivariée réalisée sur l’ensemble de la base de données (environ 1 million d’images PCM) a montré d'une manière quantitative que les myoblastes cultivés dans le milieu SFM étaient plus allongés et plus petits que ceux cultivés dans le milieu SSM. Les algorithmes développés grâce à ce projet pourraient être utilisés sur d'autres phénotypes cellulaires pour des applications de criblage à haut débit et de contrôle de cultures cellulaires.Automated cell detection and characterization is important in many research fields such as wound healing, embryo development, immune system studies, cancer research, parasite spreading, tissue engineering, stem cell research and drug research and testing. Studying in vitro cellular behavior via live-cell imaging and high-throughput screening involves thousands of images and vast amounts of data, and automated analysis tools relying on machine vision methods and non-intrusive methods such as phase contrast microscopy (PCM) are a necessity. However, there are still some challenges to overcome, since PCM images are difficult to analyze because of the bright halo surrounding the cells and blurry cell-cell boundaries when they are touching. The goal of this project was to develop image processing algorithms to analyze PCM images in an automated fashion, capable of processing large datasets of images to extract information related to cellular viability and morphology. To develop these algorithms, a large dataset of myoblasts images acquired in live-cell imaging (in PCM) was created, growing the cells in either a serum-supplemented (SSM) or a serum-free (SFM) medium over several passages. As a result, algorithms capable of computing the cell-covered surface and cellular morphological features were programmed in Matlab®. The cell-covered surface was estimated using a range filter, a threshold and a minimum cut size in order to look at the cellular growth kinetics. Results showed that the cells were growing at similar paces for both media, but their growth rate was decreasing linearly with passage number. The undecimated wavelet transform multivariate image analysis (UWT-MIA) method was developed, and was used to estimate cellular morphological features distributions (major axis, minor axis, orientation and roundness distributions) on a very large PCM image dataset using the Gabor continuous wavelet transform. Multivariate data analysis performed on the whole database (around 1 million PCM images) showed in a quantitative manner that myoblasts grown in SFM were more elongated and smaller than cells grown in SSM. The algorithms developed through this project could be used in the future on other cellular phenotypes for high-throughput screening and cell culture control applications