510 research outputs found
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
Automatic Denoising and Unmixing in Hyperspectral Image Processing
This thesis addresses two important aspects in hyperspectral image processing: automatic hyperspectral image denoising and unmixing. The first part of this thesis is devoted to a novel automatic optimized vector bilateral filter denoising algorithm, while the remainder concerns nonnegative matrix factorization with deterministic annealing for unsupervised unmixing in remote sensing hyperspectral images. The need for automatic hyperspectral image processing has been promoted by the development of potent hyperspectral systems, with hundreds of narrow contiguous bands, spanning the visible to the long wave infrared range of the electromagnetic spectrum. Due to the large volume of raw data generated by such sensors, automatic processing in the hyperspectral images processing chain is preferred to minimize human workload and achieve optimal result. Two of the mostly researched processing for such automatic effort are: hyperspectral image denoising, which is an important preprocessing step for almost all remote sensing tasks, and unsupervised unmixing, which decomposes the pixel spectra into a collection of endmember spectral signatures and their corresponding abundance fractions. Two new methodologies are introduced in this thesis to tackle the automatic processing problems described above.
Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios. Typical vector bilateral filtering usage does not employ parameters that have been determined to satisfy optimality criteria. This thesis also introduces an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimizing the Stein\u27s unbiased risk estimate (SURE) of this nonlinear estimator. Along the way, this thesis provides a plausibility argument with an analytical example as to why vector bilateral filtering outperforms band-wise 2D bilateral filtering in enhancing SNR. Experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images when compared to several other approaches.
Non-negative matrix factorization (NMF) technique and its extensions were developed to find part based, linear representations of non-negative multivariate data. They have been shown to provide more interpretable results with realistic non-negative constrain in unsupervised learning applications such as hyperspectral imagery unmixing, image feature extraction, and data mining. This thesis extends the NMF method by incorporating deterministic annealing optimization procedure, which will help solve the non-convexity problem in NMF and provide a better choice of sparseness constrain. The approach is based on replacing the difficult non-convex optimization problem of NMF with an easier one by adding an auxiliary convex entropy constrain term and solving this first. Experiment results with hyperspectral unmixing application show that the proposed technique provides improved unmixing performance compared to other state-of-the-art methods
Mineral identification using data-mining in hyperspectral infrared imagery
Les applications de lâimagerie infrarouge dans le domaine de la gĂ©ologie sont principalement des applications hyperspectrales. Elles permettent entre autre lâidentification minĂ©rale, la cartographie, ainsi que lâestimation de la portĂ©e. Le plus souvent, ces acquisitions sont rĂ©alisĂ©es in-situ soit Ă lâaide de capteurs aĂ©roportĂ©s, soit Ă lâaide de dispositifs portatifs. La dĂ©couverte de minĂ©raux indicateurs a permis dâamĂ©liorer grandement lâexploration minĂ©rale. Ceci est en partie dĂ» Ă lâutilisation dâinstruments portatifs. Dans ce contexte le dĂ©veloppement de systĂšmes automatisĂ©s permettrait dâaugmenter Ă la fois la qualitĂ© de lâexploration et la prĂ©cision de la dĂ©tection des indicateurs. Câest dans ce cadre que sâinscrit le travail menĂ© dans ce doctorat. Le sujet consistait en lâutilisation de mĂ©thodes dâapprentissage automatique appliquĂ©es Ă lâanalyse (au traitement) dâimages hyperspectrales prises dans les longueurs dâonde infrarouge. Lâobjectif recherchĂ© Ă©tant lâidentification de grains minĂ©raux de petites tailles utilisĂ©s comme indicateurs minĂ©ral -ogiques. Une application potentielle de cette recherche serait le dĂ©veloppement dâun outil logiciel dâassistance pour lâanalyse des Ă©chantillons lors de lâexploration minĂ©rale. Les expĂ©riences ont Ă©tĂ© menĂ©es en laboratoire dans la gamme relative Ă lâinfrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m Ă 11.8 m. Ces essais ont permis de proposer une mĂ©thode pour calculer lâannulation du continuum. La mĂ©thode utilisĂ©e lors de ces essais utilise la factorisation matricielle non nĂ©gative (NMF). En utlisant une factorisation du premier ordre on peut dĂ©duire le rayonnement de pĂ©nĂ©tration, lequel peut ensuite ĂȘtre comparĂ© et analysĂ© par rapport Ă dâautres mĂ©thodes plus communes. Lâanalyse des rĂ©sultats spectraux en comparaison avec plusieurs bibliothĂšques existantes de donnĂ©es a permis de mettre en Ă©vidence la suppression du continuum. Les expĂ©rience ayant menĂ©s Ă ce rĂ©sultat ont Ă©tĂ© conduites en utilisant une plaque Infragold ainsi quâun objectif macro LWIR. Lâidentification automatique de grains de diffĂ©rents matĂ©riaux tels que la pyrope, lâolivine et le quartz a commencĂ©. Lors dâune phase de comparaison entre des approches supervisĂ©es et non supervisĂ©es, cette derniĂšre sâest montrĂ©e plus appropriĂ© en raison du comportement indĂ©pendant par rapport Ă lâĂ©tape dâentraĂźnement. Afin de confirmer la qualitĂ© de ces rĂ©sultats quatre expĂ©riences ont Ă©tĂ© menĂ©es. Lors dâune premiĂšre expĂ©rience deux algorithmes ont Ă©tĂ© Ă©valuĂ©s pour application de regroupements en utilisant lâapproche FCC (False Colour Composite). Cet essai a permis dâobserver une vitesse de convergence, jusquâa vingt fois plus rapide, ainsi quâune efficacitĂ© significativement accrue concernant lâidentification en comparaison des rĂ©sultats de la littĂ©rature. Cependant des essais effectuĂ©s sur des donnĂ©es LWIR ont montrĂ© un manque de prĂ©diction de la surface du grain lorsque les grains Ă©taient irrĂ©guliers avec prĂ©sence dâagrĂ©gats minĂ©raux. La seconde expĂ©rience a consistĂ©, en une analyse quantitaive comparative entre deux bases de donnĂ©es de Ground Truth (GT), nommĂ©e rigid-GT et observed-GT (rigide-GT: Ă©tiquet manuel de la rĂ©gion, observĂ©e-GT:Ă©tiquetage manuel les pixels). La prĂ©cision des rĂ©sultats Ă©tait 1.5 fois meilleur lorsque lâon a utlisĂ© la base de donnĂ©es observed-GT que rigid-GT. Pour les deux derniĂšres epxĂ©rience, des donnĂ©es venant dâun MEB (Microscope Ălectronique Ă Balayage) ainsi que dâun microscopie Ă fluorescence (XRF) ont Ă©tĂ© ajoutĂ©es. Ces donnĂ©es ont permis dâintroduire des informations relatives tant aux agrĂ©gats minĂ©raux quâĂ la surface des grains. Les rĂ©sultats ont Ă©tĂ© comparĂ©s par des techniques dâidentification automatique des minĂ©raux, utilisant ArcGIS. Cette derniĂšre a montrĂ© une performance prometteuse quand Ă lâidentification automatique et Ă aussi Ă©tĂ© utilisĂ©e pour la GT de validation. Dans lâensemble, les quatre mĂ©thodes de cette thĂšse reprĂ©sentent des mĂ©thodologies bĂ©nĂ©fiques pour lâidentification des minĂ©raux. Ces mĂ©thodes prĂ©sentent lâavantage dâĂȘtre non-destructives, relativement prĂ©cises et dâavoir un faible coĂ»t en temps calcul ce qui pourrait les qualifier pour ĂȘtre utilisĂ©e dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7ÎŒm to 11.8ÎŒm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grainâs surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grainâs surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
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