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

PENGELOMPOKAN KABUPATEN/KOTA DI PULAU KALIMANTAN BERDASARKAN INDIKATOR KESEJAHTERAAN RAKYAT MENGGUNAKAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS

Cluster analysis has the aim of grouping several objects of observation based on the data found in the information to describe the objects and their relationships. The grouping method used in this research is the Fuzzy C-Means (FCM) and Subtractive Fuzzy C-Means (SFCM) methods. The two grouping methods were applied to the people's welfare indicator data in 42 regencies/cities on the island of Kalimantan. The purpose of this study was to obtain the results of grouping districts/cities on the island of Kalimantan based on indicators of people's welfare and to obtain the results of a comparison of the FCM and SFCM methods. Based on the results of the analysis, the FCM and SFCM methods yield the same conclusions, so that in this study the FCM and SFCM methods are both good to use in classifying districts/cities on the island of Kalimantan based on people's welfare indicators and produce an optimal cluster of two clusters, namely the first cluster consisting of 10 Regencies/Cities on the island of Kalimantan, while the second cluster consists of 32 districts/cities on the island of Borneo.Analisis klaster mempunyai tujuan untuk mengelompokkan beberapa objek pengamatan berdasarkan data yang ditemukan dalam informasi untuk menggambarkan objek dan hubungannya. Metode pengelompokan yang digunakan dalam penelitian ini adalah metode Fuzzy C-Means (FCM) dan Subtractive Fuzzy C-Means (SFCM). Dua metode pengelompokan tersebut diterapkan pada data indikator kesejahteraan rakyat pada 42 Kabupaten/Kota di Pulau Kalimantan. Tujuan dari penelitian ini adalah untuk mendapatkan hasil pengelompokan Kabupaten/Kota di Pulau Kalimantan berdasarkan indikator kesejahteraan rakyat serta untuk memperoleh hasil perbandingan metode FCM dan SFCM. Berdasarkan hasil analisis, metode FCM dan SFCM menghasilkan kesimpulan yang sama sehingga pada penelitian ini metode FCM dan SFCM sama-sama baik untuk digunakan dalam mengelompokkan Kabupaten/Kota di Pulau Kalimantan berdasarkan indikator kesejahteraan rakyat dan menghasilkan klaster optimal sebanyak dua klaster yaitu klaster pertama beranggotakan 10 Kabupaten/Kota di Pulau Kalimantan sedangkan klaster kedua beranggotakan 32 Kabupaten/Kota di Pulau Kalimantan

STABLE ADAPTIVE CONTROL FOR A CLASS OF NONLINEAR SYSTEMS WITHOUT USE OF A SUPERVISORY TERM IN THE CONTROL LAW

In this paper, a direct adaptive control scheme for a class of nonlinear systems is proposed. The architecture employs a Gaussian radial basis function (RBF) network to construct an adaptive controller. The parameters of the adaptive controller are adapted and changed according to a law derived using Lyapunov stability theory. The centres of the RBF network are adapted on line using the k-means algorithm. Asymptotic Lyapunov stability is established without the use of a supervisory (compensatory) term in the control law and with the tracking errors converging to a neighbourhood of the origin. Finally, a simulation is provided to explore the feasibility of the proposed neuronal controller design method

An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market

In recent years there has been a closer interrelationship between several scientific areas trying to obtain a more realistic and rich explanation of the natural and social phenomena. Among these it should be emphasized the increasing interrelationship between physics and financial theory. In this field the analysis of uncertainty, which is crucial in financial analysis, can be made using measures of physics statistics and information theory, namely the Shannon entropy. One advantage of this approach is that the entropy is a more general measure than the variance, since it accounts for higher order moments of a probability distribution function. An empirical application was made using data collected from the Portuguese Stock Market.Comment: 8 pages, 2 figures, presented in the conference Next Sigma-Phi 200

Clustering analysis for gene expression data: a methodological review

Clustering is one of most useful tools for the microarray gene expression data analysis. Although there have been many reviews and surveys in the literature, many good and effective clustering ideas have not been collected in a systematic way for some reasons. In this paper, we review five clustering families representing five clustering concepts rather than five algorithms. We also review some clustering validations and collect a list of benchmark gene expression datasets