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

The knowledge-based software assistant

Where the Knowledge Based Software Assistant (KBSA) is now, four years after the initial report, is discussed. Also described is what the Rome Air Development Center expects at the end of the first contract iteration. What the second and third contract iterations will look like are characterized

DAG-based software frameworks for PDEs

pre-printThe task-based approach to software and parallelism is well-known and has been proposed as a potential candidate, named the silver model, for exas-cale software. This approach is not yet widely used in the large-scale multi-core parallel computing of complex systems of partial differential equations. After surveying task-based approaches we investigate how well the Uintah software and an extension named Wasatch fit in the task-based paradigm and how well they perform on large scale parallel computers. The conclusion is that these approaches show great promise for petascale but that considerable algorithmic challenges remain

Design of testbed and emulation tools

The research summarized was concerned with the design of testbed and emulation tools suitable to assist in projecting, with reasonable accuracy, the expected performance of highly concurrent computing systems on large, complete applications. Such testbed and emulation tools are intended for the eventual use of those exploring new concurrent system architectures and organizations, either as users or as designers of such systems. While a range of alternatives was considered, a software based set of hierarchical tools was chosen to provide maximum flexibility, to ease in moving to new computers as technology improves and to take advantage of the inherent reliability and availability of commercially available computing systems

Hyperspectral Image Unmixing Incorporating Adjacency Information

While the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials’ spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results

Design and Implementation of Parallel FIR Filter Using High Speed Vedic Multiplier

The demand for high speed processing has been increasing as a result of expanding computer and signal processing applications. Higher throughput arithmetic operations are important to achieve the desired performance in many signal processing and image processing applications. One of the key arithmetic operations in such applications is multiplication which determines the performance of the entire system. Thus the optimization of the multiplier speed and area is a challenge for many processors. This challenge has been successfully overcome by the use of ancient Vedic multiplier. This paper illustrates design and implementation of parallel Finite Impulse Response (FIR) filters using Vedic mathematics based Urdhva Tiryabhyam algorithm. The system is aiming to reduced propagation delay and area of the filter. The proposed system based on Vedic multiplier is compared with that on conventional multiplier on the basis of resources and time required for processing given data. The comparison shows the 36.29% and 15.70% reduction in propagation delay for two-parallel and three-parallel FIR filter using Vedic multiplier as compared to that of conventional multiplier. The architecture is coded in VHDL and synthesized and simulated by using Xilinx Design Suite 13.1 ISE

Large scale numerical software development using functional languages

PhD ThesisFunctional programming languages such as Haskell allow numerical algorithms to be expressed in a concise, machine-independent manner that closely reflects the underlying mathematical notation in which the algorithm is described. Unfortunately the price paid for this level of abstraction is usually a considerable increase in execution time and space usage. This thesis presents a three-part study of the use of modern purely-functional languages to develop numerical software. In Part I the appropriateness and usefulness of language features such as polymorphism. pattern matching, type-class overloading and non-strict semantics are discussed together with the limitations they impose. Quantitative statistics concerning the manner in which these features are used in practice are also presented. In Part II the information gathered from Part I is used to design and implement FSC. all experimental functional language tailored to numerical computing, motivated as much by pragmatic as theoretical issues. This language is then used to develop numerical software and its suitability assessed via benchmarking it against C/C++ and Haskell under various metrics. In Part III the work is summarised and assessed.EPSRC