Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

Reliable link quality estimation in low-power wireless networks and its impact on tree-routing

Radio link quality estimation is essential for protocols and mechanisms such as routing, mobility management and localization, particularly for low-power wireless networks such as wireless sensor networks. Commodity Link Quality Estimators (LQEs), e.g. PRR, RNP, ETX, four-bit and RSSI, can only provide a partial characterization of links as they ignore several link properties such as channel quality and stability. In this paper, we propose F-LQE (Fuzzy Link Quality Estimator, a holistic metric that estimates link quality on the basis of four link quality properties—packet delivery, asymmetry, stability, and channel quality—that are expressed and combined using Fuzzy Logic. We demonstrate through an extensive experimental analysis that F-LQE is more reliable than existing estimators (e.g., PRR, WMEWMA, ETX, RNP, and four-bit) as it provides a finer grain link classification. It is also more stable as it has lower coefficient of variation of link estimates. Importantly, we evaluate the impact of F-LQE on the performance of tree routing, specifically the CTP (Collection Tree Protocol). For this purpose, we adapted F-LQE to build a new routing metric for CTP, which we dubbed as F-LQE/RM. Extensive experimental results obtained with state-of-the-art widely used test-beds show that F-LQE/RM improves significantly CTP routing performance over four-bit (the default LQE of CTP) and ETX (another popular LQE). F-LQE/RM improves the end-to-end packet delivery by up to 16%, reduces the number of packet retransmissions by up to 32%, reduces the Hop count by up to 4%, and improves the topology stability by up to 47%.

Formation Control Driven by Cooperative Object Tracking

In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles.

Accounting accruals and information asymmetry in Europe

We investigate whether the positive relation between accounting accruals and information asymmetry documented for U.S. stock markets also holds for European markets, considered as a whole and at the country level. This research is relevant because this relation is likely to be affected by differences in accounting standards used by companies for financial reporting, in the traditional use of the banking system or capital markets for firm financing, in legal systems and cultural environment. We find that in European stock markets discretionary accruals are positively related with the Corwin and Schultz high-low spread estimator used as a proxy for information asymmetry. Our results suggest that the earnings management component of accruals outweighs the informational component, but the significance of the relation varies across countries. Further, such association tends to be stronger for firms with the highest levels of positive discretionary accruals. Consistent with the evidence provided by the authors, our results also suggest that the high-low spread estimator is more efficient than the closing bid-ask spread when analysing the impact of information quality on information asymmetry.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

A informação contabilística no processo de tomada de decisão de microempresas

A globalização, o desenvolvimento económico e o aumento da competitividade, possibilitaram um acréscimo do número de microempresas, originado o aumento do peso que estas detêm na economia. Estas absorvem a maioria da mão-de-obra do setor privado e criam parte significativa da riqueza de um país, no entanto, apresentam elevados índices de dissolução e liquidação, pondo em causa o desenvolvimento sustentável das economias onde estão sediadas. A informação contabilística é essencial em qualquer tipo de organização. Para as microempresas a utilização da informação contabilística no processo de tomada de decisão determina a diferença entre sucesso e o insucesso do empreendimento, no entanto, este tipo de informação continua a ser menosprezado pela gerência das microempresas. É assim importante estudar os fatores que limitam a utilização da informação contabilística no processo de tomada de decisão de microempresas e investigar a sua relação com os fatores de insucesso deste tipo de entidades. Nesta investigação para se atingirem os objetivos propostos considera-se adequado a adoção de uma metodologia de natureza quantitativa, assente num inquérito dirigido aos Contabilistas Certificados. Através da investigação demonstrou-se a importância que a informação contabilística detém no processo de tomada de decisão das microempresas Portuguesas. Constatou-se que os gerentes limitam a utilização deste tipo de informação, uma vez que não têm capacidade para interpretar a informação contabilística, nem reconhecem os benefícios inerentes à sua utilização nas decisões empresariais. Confirmou-se ainda que existe uma relação positiva entre os fatores de insucesso e os fatores que limitam a utilização da informação contabilística no processo de tomada de decisão de microempresas.

Thermal shock – and its perfect imperfections

Dissertação de Mestrado em Arte e Ciência do Vidro

The African studies course of the Instituto de Estudos Superiores Militares. An analysis of the correlations and conflictive character inside the triangle - Development, environment and security - in Africa’s Nigeria

Relatório de Estágio de Mestrado em Ciência Política e Relações Internacionais Globalização e Ambiente

Gastrointestinal symbionts of chimpanzees in Cantanhez National Park, Guinea-Bissau with respect to habitat fragmentation

One of the major factors threatening chimpanzees (Pan troglodytes verus) in Guinea-Bissau is habitat fragmentation. Such fragmentation may cause changes in symbiont dynamics resulting in increased susceptibility to infection, changes in host specificity and virulence. We monitored gastrointestinal symbiotic fauna of three chimpanzee subpopulations living within Cantanhez National Park (CNP) in Guinea Bissau in the areas with different levels of anthropogenic fragmentation. Using standard coproscopical methods (merthiolate-iodine formalin concentration and Sheather's flotation) we examined 102 fecal samples and identified at least 13 different symbiotic genera (Troglodytella abrassarti, Troglocorys cava, Blastocystis spp., Entamoeba spp., Iodamoeba butschlii, Giardia intestinalis, Chilomastix mesnili, Bertiella sp., Probstmayria gombensis, unidentified strongylids, Strongyloides stercoralis, Strongyloides fuelleborni, and Trichuris sp.). The symbiotic fauna of the CNP chimpanzees is comparable to that reported for other wild chimpanzee populations, although CNP chimpanzees have a higher prevalence of Trichuris sp. Symbiont richness was higher in chimpanzee subpopulations living in fragmented forests compared to the community inhabiting continuous forest area. We reported significantly higher prevalence of G. intestinalis in chimpanzees from fragmented areas, which could be attributed to increased contact with humans and livestock.

Parameter estimation in the presence of auxiliary information

Dissertação para obtenção do Grau de Doutora em Estatística e Gestão de Risco, Especialidade em Estatística

Hospital-acquired infections: a cost estimation for CLABSI in Portugal

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Development of a satellite based PM10 concentration product for urban areas

Dissertação para obtenção do Grau de Doutor em Engenharia do Ambiente

Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic.

Return Migration, Self-Selection and Entrepreneurship in Mozambique

Does return migration affect entrepreneurship? This question has important implications for the debate on the economic development effects of migration for origin countries. The existing literature has, however, not addressed how the estimation of the impact of return migration on entrepreneurship is affected by double unobservable migrant self-selection, both at the initial outward migration and at the final inward return migration stages. This paper uses a representative household survey conducted in Mozambique in order to address this research question. We exploit variation provided by displacement caused by civil war in Mozambique, as well as social unrest and other shocks in migrant destination countries. The results lend support to negative unobservable self-selection at both and each of the initial and return stages of migration, which results in an under-estimation of the effects of return migration on entrepreneurial outcomes when using a ‘naïve’ estimator not controlling for self-selection. Indeed, ‘naïve’ estimates point to a 13 pp increase in the probability of owning a business when there is a return migrant in the household relative to non-migrants only, whereas excluding the double effect of unobservable self-selection, this effect becomes significantly larger - between 24 pp and 29 pp, depending on the method of estimation and source of variation used.