Robust Linear Spectral Unmixing using Anomaly Detection

This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modelling anomalies and additive Gaussian noise. A Markov random field is used for anomaly detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and anomaly detection algorithm. Simulations conducted with synthetic and real hyperspectral images demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images

Manifold learning based spectral unmixing of hyperspectral remote sensing data

Nonlinear mixing effects inherent in hyperspectral data are not properly represented in linear spectral unmixing models. Although direct nonlinear unmixing models provide capability to capture nonlinear phenomena, they are difficult to formulate and the results are not always generalizable. Manifold learning based spectral unmixing accommodates nonlinearity in the data in the feature extraction stage followed by linear mixing, thereby incorporating some characteristics of nonlinearity while retaining advantages of linear unmixing approaches. Since endmember selection is critical to successful spectral unmixing, it is important to select proper endmembers from the manifold space. However, excessive computational burden hinders development of manifolds for large-scale remote sensing datasets. This dissertation addresses issues related to high computational overhead requirements of manifold learning for developing representative manifolds for the spectral unmixing task. Manifold approximations using landmarks are popular for mitigating the computational complexity of manifold learning. A new computationally effective landmark selection method that exploits spatial redundancy in the imagery is proposed. A robust, less costly landmark set with low spectral and spatial redundancy is successfully incorporated with a hybrid manifold which shares properties of both global and local manifolds. While landmark methods reduce computational demand, the resulting manifolds may not represent subtle features of the manifold adequately. Active learning heuristics are introduced to increase the number of landmarks, with the goal of developing more representative manifolds for spectral unmixing. By communicating between the landmark set and the query criteria relative to spectral unmixing, more representative and stable manifolds with less spectrally and spatially redundant landmarks are developed. A new ranking method based on the pixels with locally high spectral variability within image subsets and convex-geometry finds a solution more quickly and precisely. Experiments were conducted to evaluate the proposed methods using the AVIRIS Cuprite hyperspectral reference dataset. A case study of manifold learning based spectral unmixing in agricultural areas is included in the dissertation.Remotely sensed data collected by airborne or spaceborne sensors are utilized to quantify crop residue cover over an extensive area. Although remote sensing indices are popular for characterizing residue amounts, they are not effective with noisy Hyperion data because the effect of residual striping artifacts is amplified in ratios involving band differences. In this case study, spectral unmixing techniques are investigated for estimating crop residue as an alternative approach to empirical models developed using band based indices. The spectral unmixing techniques, and especially the manifold learning approaches, provide more robust, lower RMSE estimates for crop residue cover than the hyperspectral index based method for Hyperion data

Analysis of compressive sensing for hyperspectral remote sensing applications

Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spectral and spatial dimensions. This thesis utilizes a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) to simulate CS measurements from traditionally sensed HyMap images. A novel reconstruction algorithm that combines spectral smoothing and spatial total variation (TV) is used to create high resolution hyperspectral imagery from the simulated CS measurements. This research examines the effect of the number of measurements, which corresponds to the percentage of physical data sampled, on the quality of simulated CS data as estimated through performance of spectral image processing algorithms. The effect of CS on the data cloud is explored through principal component analysis (PCA) and endmember extraction. The ultimate purpose of this thesis is to investigate the utility of the CS sensor model and reconstruction for various hyperspectral applications in order to identify the strengths and limitations of CS. While CS is shown to create useful imagery for visual analysis, the data cloud is altered and per-pixel spectral fidelity declines for CS reconstructions from only a small number of measurements. In some hyperspectral applications, many measurements are needed in order to obtain comparable results to traditionally sensed HSI, including atmospheric compensation and subpixel target detection. On the other hand, in hyperspectral applications where pixels must be dramatically altered in order to be misclassified, such as land classification or NDVI mapping, CS shows promise

DETERMINING WHERE INDIVIDUAL VEHICLES SHOULD NOT DRIVE IN SEMIARID TERRAIN IN VIRGINIA CITY, NV

This thesis explored elements involved in determining and mapping where a vehicle should not drive off-road in semiarid areas. Obstacles are anything which slows or obstructs progress (Meyer et al., 1977) or limits the space available for maneuvering (Spenko et al., 2006). This study identified the major factors relevant in determining which terrain features should be considered obstacles when off-road driving and thus should be avoided. These are elements relating to the vehicle itself and how it is driven as well as terrain factors of slope, vegetation, water, and soil. Identification of these in the terrain was done using inferential methods of Terrain Pattern Recognition (TPR), analyzing of remotely sensing data, and Digital Elevation Map (DEM) data analysis. Analysis was further refined using other reference information about the area. Other factors such as weather, driving angle, and environmental impact are discussed. This information was applied to a section of Virginia City, Nevada as a case-study. Analysis and mapping was done purposely without field work prior to mapping to determine what could be assessed using only remote means. Not all findings from the literature review could be implemented in this trafficability study. Some methods and trafficability knowledge could not be implemented and were omitted due to data being unavailable, un-acquirable, or being too coarsely mapped to be useful. Examples of these are Lidar mapping of the area, soil profiling of the terrain, and assessment of plant species present in the area for driven-over traction and tire punctures. The Virginia City section was analyzed and mapped utilizing hyperspectral remotely sensed image data, remote-sensor-derived DEM data was used in a Geographical Information Systems (GIS). Stereo-paired air photos of the study site were used in TPR. Other information on flora, historical weather, and a previous soil survey map were used in a Geographical Information System (GIS). Field validation was used to check findings.The case study's trafficability assessment demonstrated methodologies of terrain analysis which successfully classified many materials present and identified major areas where a vehicle should not drive. The methods used were: Manual TPR of the stereo-paired air photo using a stereo photo viewer to conduct drainage-tracing and slope analysis of the DEM was done using automated methods in ArcMap. The SpecTIR hyperspectral data was analyzed using the manual Environment for Visualizing Images (ENVI) software hourglass procedure. Visual analysis of the hyperspectral data and air photos along with known soil and vegetation characteristics were used to refine analyses. Processed data was georectified using SpecTIR Geographic Lookup Table (GLT) input geometry, and exported to and analyzed in ArcMap with the other data previously listed. Features were identified based on their spectral attributes, spatial properties, and through visual analysis. Inaccuracies in mapping were attributable largely to spatial resolution of Digital Elevation Maps (DEMs) which averaged out some non-drivable obstacles and parts of a drivable road, subjective human and computer decisions during the processing of the data, and grouping of spectral end-members during hyperspectral data analysis. Further refinements to the mapping process could have been made if fieldwork was done during the mapping process.Mapping and field validation found: several manmade and natural obstacles were visible from the ground, but these obstacles were too fine, thin, or small to be identified from the remote sensing data. . Examples are fences and some natural terrain surface roughness - where the terrain's surface deviated from being a smooth surface, exhibiting micro- variations in surface elevation and/or textures. Slope analysis using the 10-meter and 30-meter resolution DEMs did not accurately depict some manmade features [eg. some of the buildings, portions of roads, and fences], evident with a well-trafficked paved road showing in DEM analysis as having too steep a slope [beyond 15°] to be drivable. Some features had been spectrally grouped together during analysis, due to similar spectral properties. Spectral grouping is a process where the spectral class's pixel areas are reviewed and classes which have too few occurrences are averaged into similar classes or dropped entirely. This is done to reduce the number of spectrally unique material classes to those that are most relevant to the terrain mapped. These decisions are subjective and in one case two similar spectral material classes were combined. In later evaluation should have remained as two separate material classes. In field sample collection, some of the determined features; free-standing water and liquid tanks, were found to be inaccessible due to being on private land and/or fence secured. These had to be visually verified - photos were also taken. Further refinements to the mapping could have been made if fieldwork was done during the mapping process. Determining and mapping where a vehicle should not drive in semiarid areas is a complex task which involves many variables and reference data types. Processing, analyzing, and fusing these different references entails subjective manual and automated decisions which are subject to errors and/or inaccuracies at multiple levels that can individually or collectively skew results, causing terrain trafficability to be depicted incorrectly. That said, a usable reference map is creatable which can assist decision makers when determining their route(s)

BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. Deep neural networks (DNNs), including autoencoders (AEs), convolutional neural networks (CNNs) and vision transformers (ViTs), have shown remarkable performance in this field due to their powerful ability to model the complicated background. However, for reconstruction tasks, DNNs tend to incorporate both background and anomalies into the estimated background, which is referred to as the identical mapping problem (IMP) and leads to significantly decreased performance. To address this limitation, we propose a model-independent binary mask-guided separation training strategy for DNNs, named BiGSeT. Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image. The background is preserved, while the potential anomalies are suppressed by using an efficient second-order Laplacian of Gaussian (LoG) operator, generating a pure background estimate. In order to maintain separability during training, we periodically update the mask using a robust proportion threshold estimated before the training. In our experiments, We adopt a vanilla AE as the network to validate our training strategy on several real-world datasets. Our results show superior performance compared to some state-of-the-art methods. Specifically, we achieved a 90.67% AUC score on the HyMap Cooke City dataset. Additionally, we applied our training strategy to other deep network structures, achieving improved detection performance compared to their original versions, demonstrating its effective transferability. The code of our method will be available at https://github.com/enter-i-username/BiGSeT.Comment: 13 pages, 13 figures, submitted to IEEE TRANSACTIONS ON IMAGE PROCESSIN

Interpreting Vegetation and Soil Anomalies in the Guarumen Area of Northwestern Venezuela Using Remote Sensing Applications

The Guarumen area of Venezuela is a tectonically active region that is approximately 1,640 mi2 across the northern portions of the Barinas Basin and the foothills of the Mérida Andes. It is structurally influenced by the Caribbean plate to the north, the Nazca plate to the west, and the Maracaibo block against the Guyana Shield of the South American Plate. These result in an oblique boundary that gives rise to the fold-and-thrust belt of the Mérida Andes to the west, and the Caribbean Mountain system to the north, in concordance to the right-lateral shearing that is evidenced by the Boconó fault system. The goal of this research was to investigate the geological setting of northwestern Venezuela and further understand the geologic controls of the region, as it has become a region of interest for mineral, oil, and gas exploration. To achieve the goal, hyperspectral and multispectral data analysis were used to address land cover types by reducing hyperspectral and multispectral spectra to unique endmembers for use in classification. Then, provide an accurate land cover analysis using derived endmembers to characterize the outcomes concerning the influence of geological phenomena, and determine if microclimate analysis using satellite-based land surface temperature data can be effectively used to infer geologic structure or geomorphology, particularly soils and vegetation. Based on the hyperspectral data, an in-depth endmember analysis was conducted with image-derived spectra. These spectra were plotted in comparison with spectral libraries to identify the anomaly classification. It was determined that the natural vegetation make up of a specific region helped identify soil type. The Guarumen area was influenced by the sediment transport of the alluvial stream geomorphology of both the Merida Andes and the Caribbean Mountain System and both its respective geologies. The microclimate analysis shoa land surface temperature comparison of two separate Landscenes. Both shoa similar mean temperature range due to Venezuela’s tropical climate, but differed in other classifications. Results from this research show that remote sensing applications with limited field data can provide accurate land cover analysis concerning geological phenomena, but further field analysis is needed for more detailed classification

Caracterização e estudo comparativo de exsudações de hidrocarbonetos e plays petrolíferos em bacias terrestres das regiões central do Irã e sudeste do Brasil usando sensoriamento remoto espectral

Orientador: Carlos Roberto de Souza FilhoTese (doutorado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: O objetivo desta pesquisa foi explorar as assinaturas de exsudações de hidrocarbonetos na superfície usando a tecnologia de detecção remota espectral. Isso foi alcançado primeiro, realizando uma revisão abrangente das capacidades e potenciais técnicas de detecção direta e indireta. Em seguida, a técnica foi aplicada para investigar dois locais de teste localizados no Irã e no Brasil, conhecidos por hospedar sistemas ativos de micro-exsudações e afloramentos betuminosos, respectivamente. A primeira área de estudo está localizada perto da cidade de Qom (Irã), e está inserida no campo petrolífero Alborz, enterrado sob sedimentos datados do Oligoceno da Formação Upper Red. O segundo local está localizado perto da cidade de Anhembi (SP), na margem oriental da bacia do Paraná, no Brasil, e inclui acumulações de betume em arenitos triássicos da Formação Pirambóia. O trabalho na área de Qom integrou evidências de (i) estudos petrográficos e geoquímicos em laboratório, (ii) investigações de afloramentos em campo, e (iii) mapeamento de anomalia em larga escala através de conjuntos de dados multi-espectrais ASTER e Sentinel-2. O resultado deste estudo se trata de novos indicadores mineralógicos e geoquímicos para a exploração de micro-exsudações e um modelo de micro-exsudações atualizado. Durante este trabalho, conseguimos desenvolver novas metodologias para análise de dados espectroscópicos. Através da utilização de dados simulados, indicamos que o instrumento de satélite WorldView-3 tem potencial para detecção direta de hidrocarbonetos. Na sequência do estudo, dados reais sobre afloramentos de arenitos e óleo na área de Anhembi foram investigados. A área foi fotografada novamente no chão e usando o sistema de imagem hiperespectral AisaFENIX. Seguiu-se estudos e amostragem no campo,incluindo espectroscopia de alcance fechado das amostras no laboratório usando instrumentos de imagem (ou seja, sisuCHEMA) e não-imagem (ou seja, FieldSpec-4). O estudo demonstrou que uma abordagem espectroscópica multi-escala poderia fornecer uma imagem completa das variações no conteúdo e composição do betume e minerais de alteração que acompanham. A assinatura de hidrocarbonetos, especialmente a centrada em 2300 nm, mostrou-se consistente e comparável entre as escalas e capaz de estimar o teor de betume de areias de petróleo em todas as escalas de imagemAbstract: The objective of this research was to explore for the signatures of seeping hydrocarbons on the surface using spectral remote sensing technology. It was achieved firstly by conducting a comprehensive review of the capacities and potentials of the technique for direct and indirect seepage detection. Next, the technique was applied to investigate two distinctive test sites located in Iran and Brazil known to retain active microseepage systems and bituminous outcrops, respectively. The first study area is located near the city of Qom in Iran, and consists of Alborz oilfield buried under Oligocene sediments of the Upper-Red Formation. The second site is located near the town of Anhembi on the eastern edge of the Paraná Basin in Brazil and includes bitumen accumulations in the Triassic sandstones of the Pirambóia Formation. Our work in Qom area integrated evidence from (i) petrographic, spectroscopic, and geochemical studies in the laboratory, (ii) outcrop investigations in the field, and (iii) broad-scale anomaly mapping via orbital remote sensing data. The outcomes of this study was novel mineralogical and geochemical indicators for microseepage characterization and a classification scheme for the microseepage-induced alterations. Our study indicated that active microseepage systems occur in large parts of the lithofacies in Qom area, implying that the extent of the petroleum reservoir is much larger than previously thought. During this work, we also developed new methodologies for spectroscopic data analysis and processing. On the other side, by using simulated data, we indicated that WorldView-3 satellite instrument has the potential for direct hydrocarbon detection. Following this demonstration, real datasets were acquired over oil-sand outcrops of the Anhembi area. The area was further imaged on the ground and from the air by using an AisaFENIX hyperspectral imaging system. This was followed by outcrop studies and sampling in the field and close-range spectroscopy in the laboratory using both imaging (i.e. sisuCHEMA) and nonimaging instruments. The study demonstrated that a multi-scale spectroscopic approach could provide a complete picture of the variations in the content and composition of bitumen and associated alteration mineralogy. The oil signature, especially the one centered at 2300 nm, was shown to be consistent and comparable among scales, and capable of estimating the bitumen content of oil-sands at all imaging scalesDoutoradoGeologia e Recursos NaturaisDoutor em Geociências2015/06663-7FAPES

Optimized Hyperspectral Imagery Anomaly Detection Through Robust Parameter Design

Anomaly detection algorithms for hyperspectral imagery (HSI) are an important first step in the analysis chain which can reduce the overall amount of data to be processed. The actual amount of data reduced depends greatly on the accuracy of the anomaly detection algorithm implemented. Most, if not all, anomaly detection algorithms require a user to identify some initial parameters. These parameters (or controls) affect overall algorithm performance. Regardless of the anomaly detector being utilized, algorithm performance is often negatively impacted by uncontrollable noise factors which introduce additional variance into the process. In the case of HSI, the noise variables are embedded in the image under consideration. Robust parameter design (RPD) offers a method to model the controls as well as the noise variables and identify robust parameters. This research identifies image noise characteristics necessary to perform RPD on HSI. Additionally, a small sample training and test algorithm is presented. Finally, the standard RPD model is extended to consider higher order noise coefficients. Mean and variance RPD models are optimized in a dual response function suggested by Lin and Tu. Results are presented from simulations and two anomaly detection algorithms, the Reed-Xiaoli anomaly detector and the autonomous global anomaly detector

A manifold learning approach to target detection in high-resolution hyperspectral imagery

Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying targets such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m \u3c\u3c d. In the remote sensing community, this has led to a recent increase in the use of manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation; the artificial target manifold helps to guide the separation of the target data from the background data in the new, lower-dimensional manifold coordinates. Then, target detection is performed in the manifold space