Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari

The hyperspectral image analysis technique one of the most advanced remote sensing tools has been used as a possible means of identifying from a single pixel or in the field of view of the sensor An important problem in hyperspectral image processing is to decompose the mixed pixels into the information that contribute to the pixel endmember and a set of corresponding fractions of the spectral signature in the pixel abundances and this problem is known as un-mixing The effectiveness of the hyperspectral image analysis technique used in this study lies in their ability to compare a pixel spectrum with the spectra of known pure vegetation extracted from the spectral endmember selection procedures including the reflectance calibration of Landsat ETM image using ENVI software minimum noise fraction MNF pixel purity index PPI and n-dimensional visualization The Endmember extraction is one of the most fundamental and crucial tasks in hyperspectral data exploitation an ultimate goal of an endmember extraction algorithm is to find the purest form of spectrally distinct resource information of a scene The endmember extraction tendency to the type of endmembers being derived and the number of endmembers estimated by an algorithm with respect to the number of spectral bands and the number of pixels being processed also the required input data and the kind of noise if any in the signal model surveying done Results of the present study using the hyperspectral image analysis technique ascertain that Landsat ETM data can be used to generate valuable vegetative information for the District Vehari Punjab Province Pakista

Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy

Wider coverage of observation missions will increase onboard power restrictions while, at the same time, pose higher demands from the perspective of processing time, thus asking for the exploration of novel high-performance and low-power processing architectures. In this paper, we analyze the acceleration of spectral unmixing, a key technique to process hyperspectral images, on multicore architectures. To meet onboard processing restrictions, we employ a low-power Digital Signal Processor (DSP), comparing processing time and energy consumption with those of a representative set of commodity architectures. We demonstrate that DSPs offer a fair balance between ease of programming, performance, and energy consumption, resulting in a highly appealing platform to meet the restrictions of current missions if onboard processing is required

A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.The research leading to these results has received funding from the Spanish Government and European FEDER funds (DPI2012-32390), the Valencia Regional Government (PROMETEO/2013/085) and the University of Alicante (GRE12-17)

Implementação em hardware reconfigurável de método de separação de dados hiperespetrais

Relatório do Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e TelecomunicaçõesOs sensores hiperespetrais adquirem grandes quantidades de dados com uma elevada resolução espetral. Esses dados são utilizados em aplicações para classificar uma área da superfície terrestre ou detetar um determinado alvo. No entanto, existem aplicações que requerem processamento em tempo-real. Recentemente, sistemas de processamento a bordo têm surgido para reduzir a quantidade de dados a ser transmitida para as estações base e assim reduzir o atraso entre a transmissão e a análise dos dados. Sistemas esses compactos, com hardware reconfigurável, como os field programmable gate arrays (FPGAs). O presente trabalho propõe uma arquitetura num FPGA, que paraleliza o método vertex components analysis (VCA)de separação de dados hiperespetrais. Este trabalho é desenvolvido na placa ZedBoard que contém um Xilinx Zynq R -7000 XC7Z020. Na primeira fase realiza-se uma análise ao desempenho do método sem o pre--processamento de redução de dados, em termos espetrais. O método é otimizado, para reduzir o seu peso e complexidade computacional. O processo de ortogonalização é a parte mais pesada do método, é realizada por uma decomposição de valores singulares (singular value decomposition - SVD). Este processo é simplificado por uma decomposição QR que reutiliza os vetores ortogonais já determinados. É ainda analisado o tipo de precisão que o método necessita para manter o mesmo desempenho e é concluído que necessita de pelo menos 48-bit vírgula fixa ou flutuante 32-bit. Na segunda fase projeta-se uma arquitetura que paraleliza o método otimizado. Esta é escalável e consegue processar vários píxeis e/ou bandas espetrais em paralelo. A arquitetura é implementada e dimensionada para o sensor AVIRIS, onde este captura 512 píxeis com 224 bandas espetrais em 8,3 ms e a arquitetura processa 614 píxeis e determina oito assinaturas espetrais em 1,57 ms, ou seja, a arquitetura implementada é apropriada para processamento em tempo-real de dados hiperespetrais.Abstract: The Hyperspectral sensors acquire large datasets with high spectral resolution. These datasets are used to classify or detect a specific target over an area of Earth surface. However, there are applications that require real-time processing. Recently, on-board processing systems have emerged to reduce the amount of data that is transmitted to the ground base stations and thereby reduce the delay between the transmission and data analysis. On-board systems need to be compact, such as field programmable gate arrays (FPGAs). This work presents a FPGA architecture, that parallels the vertex components analysis (VCA) method for hyperspectral unmixing data. This work is developed on a ZedBoard board, which contains a Xilinx Zynq R -7000 XC7Z020. In the first phase an analysis of the method’s performance without dimensionality reduction pre-processing step, in spectral terms, is conducted. The method have been also optimized, to reduce its computational weight and complexity. The orthogonal process, performed on the singular value decomposition (SVD) used in the original method, is the most complex part of the algorithm. This process is simplified using a QR decomposition that reuses the orthogonal vectors already determined. Its also analysed the type of precision that the method needs to maintain the same performance. In the present work it is concluded that the method requires at least 48-bit fixed-point or 32-bit floating-point. In the second phase is projected an architecture that parallels the optimized method, which is scalable and can process multiple pixels and/or spectral bands in parallel. The architecture is implemented and dimensioned to AVIRIS sensor, which acquires 512 pixels with 224 spectral bands in 8,3 ms, the architecture processes 614 pixels and extracts eight spectral signatures in 1,57 ms, therefore one can conclude that the implemented architecture is appropriated for real-time hyperspectral data processing

A Novel Methodology for Calculating Large Numbers of Symmetrical Matrices on a Graphics Processing Unit: Towards Efficient, Real-Time Hyperspectral Image Processing

Hyperspectral imagery (HSI) is often processed to identify targets of interest. Many of the quantitative analysis techniques developed for this purpose mathematically manipulate the data to derive information about the target of interest based on local spectral covariance matrices. The calculation of a local spectral covariance matrix for every pixel in a given hyperspectral data scene is so computationally intensive that real-time processing with these algorithms is not feasible with today’s general purpose processing solutions. Specialized solutions are cost prohibitive, inflexible, inaccessible, or not feasible for on-board applications. Advances in graphics processing unit (GPU) capabilities and programmability offer an opportunity for general purpose computing with access to hundreds of processing cores in a system that is affordable and accessible. The GPU also offers flexibility, accessibility and feasibility that other specialized solutions do not offer. The architecture for the NVIDIA GPU used in this research is significantly different from the architecture of other parallel computing solutions. With such a substantial change in architecture it follows that the paradigm for programming graphics hardware is significantly different from traditional serial and parallel software development paradigms. In this research a methodology for mapping an HSI target detection algorithm to the NVIDIA GPU hardware and Compute Unified Device Architecture (CUDA) Application Programming Interface (API) is developed. The RX algorithm is chosen as a representative stochastic HSI algorithm that requires the calculation of a spectral covariance matrix. The developed methodology is designed to calculate a local covariance matrix for every pixel in the input HSI data scene. A characterization of the limitations imposed by the chosen GPU is given and a path forward toward optimization of a GPU-based method for real-time HSI data processing is defined

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

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

EFFECT OF DATA TRUNCATION IN AN IMPLEMENTATION OF PIXEL CLUSTERING ON A CUSTOM COMPUTING MACHINE

We investigate the effect of truncating the precision of hyperspectral image data for the purpose of more efficiently segmenting the image using a variant of k-means clustering. We describe the implementation of the algorithm on field-programmable gate array (FPGA) hardware. Truncating the data to only a few bits per pixel in each spectral channel permits a more compact hardware design, enabling greater parallelism, and ultimately a more rapid execution. It also enables the storage of larger images in the onboard memory. In exchange for faster clustering, however, one trades off the quality of the produced segmentation. We find, however, that the clustering algorithm can tolerate considerable data truncation with little degradation in cluster quality. This robustness to truncated data can be extended by computing the cluster centers to a few more bits of precision than the data. Since there are so many more pixels than centers, the more aggressive data truncation leads to significant gains in the number of pixels that can be stored in memory and processed in hardware concurrently

High spatial resolution and hyperspectral remote sensing for mapping vegetation species in tropical rainforest

The focus of this study is on vegetation species mapping using high spatial resolution IKONOS-2 and digital Color Infrared (CIR) Aerial Photos (spatial resolution 4 m for IKONOS-2 and 20 cm for CIR) and Hyperion Hyperspectral data (spectral resolution 10 nm) in Pasoh Forest Reserve, Negeri Sembilan. Spatial and spectral separability in distinguishing vegetation species were investigated prior to vegetation species mapping to provide optimal vegetation species discrimination. A total of 88 selected vegetation species and common timber groups of the dominant family Dipterocarpaceae with diameter at breast height more than 30 cm were used in this study, where trees spectra were collected by both in situ and laboratory measurements of foliar samples. The trees spectra were analysed using first and second order derivative analysis together with scatter matrix plot based on multiobjective optimization algorithm to identify the best separability and sensitive wavelength portions for vegetation species mapping. In high spatial resolution data mapping, both IKONOS-2 and CIR data were classified by supervised classification approach using maximum likelihood and neural network classifiers, while the Hyperion data was classified by spectral angle mapper and linear mixture modeling. Results of this study indicate that only a total of ten common timber group of dominant Dipterocarpaceae genus were able to be recognized at significant divergence. Both high spatial resolution data (IKONOS-2 and CIR) gave very good classification accuracy of more than 83%. The classified hyperspectral data at 30 m spatial resolution gave a classification accuracy of 65%, hence confirming that spatial resolution is more sensitive in identification of tree genus. However, for species mapping, both high spatial and spectral remotely sensed data used are marginally less sensitive than at genus level