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

Parallel Nonnegative Matrix Factorization Algorithms for Hyperspectral Images

Hyperspectral imaging is a branch of remote sensing which deals with creating and processing aerial or satellite pictures that capture wide range of wavelengths, most of which are invisible to the naked eye. Hyperspectral images are composed of many bands, each corresponding to certain light frequencies. Because of their complex nature, image processing tasks such as feature extraction can be resource and time consuming. There are many unsupervised extraction methods available. A recently investigated one is Nonnegative Matrix Factorization (NMF), a method that given positive linear matrix of positive sources, attempts to recover them. In this thesis we designed, implemented and tested parallel versions of two popular iterative NMF algorithms: one based on multiplicative updates, and another on alternative gradient computation. Our algorithms are designed to leverage the multi-processor SMP architecture and power of threading to evenly distribute the workload among the available CPU’s and improve the performance as compared to their sequential counterparts. This work could be used as a basis for creating even more powerful distributed algorithms that would work on clustered architectures. The experiments show a speedup in both algorithms without reduction in accuracy. In addition, we have also developed a java based framework offering reading and writing tools for various hyperspectral image types, as well as visualization tools, and a graphical user interface to launch and control the factorization processes

Detection And Classification Of Buried Radioactive Materials

This dissertation develops new approaches for detection and classification of buried radioactive materials. Different spectral transformation methods are proposed to effectively suppress noise and to better distinguish signal features in the transformed space. The contributions of this dissertation are detailed as follows. 1) Propose an unsupervised method for buried radioactive material detection. In the experiments, the original Reed-Xiaoli (RX) algorithm performs similarly as the gross count (GC) method; however, the constrained energy minimization (CEM) method performs better if using feature vectors selected from the RX output. Thus, an unsupervised method is developed by combining the RX and CEM methods, which can efficiently suppress the background noise when applied to the dimensionality-reduced data from principle component analysis (PCA). 2) Propose an approach for buried target detection and classification, which applies spectral transformation followed by noisejusted PCA (NAPCA). To meet the requirement of practical survey mapping, we focus on the circumstance when sensor dwell time is very short. The results show that spectral transformation can alleviate the effects from spectral noisy variation and background clutters, while NAPCA, a better choice than PCA, can extract key features for the following detection and classification. 3) Propose a particle swarm optimization (PSO)-based system to automatically determine the optimal partition for spectral transformation. Two PSOs are incorporated in the system with the outer one being responsible for selecting the optimal number of bins and the inner one for optimal bin-widths. The experimental results demonstrate that using variable bin-widths is better than a fixed bin-width, and PSO can provide better results than the traditional Powell’s method. 4) Develop parallel implementation schemes for the PSO-based spectral partition algorithm. Both cluster and graphics processing units (GPU) implementation are designed. The computational burden of serial version has been greatly reduced. The experimental results also show that GPU algorithm has similar speedup as cluster-based algorithm

Three dimensional quantitative textural analysis of nickel sulphide ore using X-ray computed tomography and grey level co-occurrence matrices on drill core

Alongside the global trend to mine and process lower grade and more mineralogically complex ores, there has been an increased awareness of the prevalence of ore heterogeneity. Ore texture - the interrelationship of minerals comprising a rock, has been identified as one of the primary geometallurgical indicators of ore variability. It is well known that a relationship exists between ore texture and the resultant metallurgical performance (ore hardness, throughput, liberation, grade, recovery). Consequently, there exists a need to rapidly, routinely, cost effectively, and reliably quantify ore texture and its variability prior to mining. This information can thereafter be incorporated into the geometallurgical block model and used for decision making informing mine planning, plant operation and optimisation, forecasting, and mine closure. The ability to rapidly, routinely, cost effectively and reliably quantify ore texture remains an ongoing challenge. In this study, the use of 3D X-ray computed tomography (XCT) is proposed as an innovative solution to non-destructively image the internal structure of drill core. Thereafter, an established, discipline independent two dimensional (2D) image analysis technique known as grey level co-occurrence matrices (GLCM) is specially adapted into three dimensions (3D) to quantify ore texture using XCT grey level volumes of drill core