Noise estimation for hyperspectral subspace identification on FPGAs

[EN] We present a reliable and efficient FPGA implementation of a procedure for the computation of the noise estimation matrix, a key stage for subspace identification of hyperspectral images. Our hardware realization is based on numerically stable orthogonal transformations, avoids the numerical difficulties of the normal equations method for the solution of linear least squares problems (LLS), and exploits the special relations between coupled LLS problems arising in the hyperspectral image. Our modular implementation decomposes the QR factorization that comprises a significant part of the cost into a sequence of suboperations, which can be efficiently computed on an FPGA.This work was supported by MINECO Projects TIN2014-53495-R and TIN2013-40968-P.León, G.; González, C.; Mayo Gual, R.; Mozos, D.; Quintana-Ortí, ES. (2019). Noise estimation for hyperspectral subspace identification on FPGAs. The Journal of Supercomputing. 75(3):1323-1335. https://doi.org/10.1007/s11227-018-2425-313231335753Anderson E et al (1999) E LAPACK users’ guide, 3rd edn. SIAM, PhiladelphiaBenner P, Novaković V, Plaza A, Quintana-Ortí ES, Remón A (2015) Fast and reliable noise estimation for Hyperspectral subspace identification. IEEE Geosci Remote Sens Lett 12(6):1199–1203Bioucas-Dias J, Nascimento J (2008) Hyperspectral subspace identification. IEEE Trans Geosci Remote Sens 46:2435–2445Bioucas-Dias J, Plaza A, Dobigeon N, Parente M, Du Q, Gader P, Chanussot J (2012) Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE JSTARS 5(2):354–379Björck A (1996) Numerical methods for least squares problems. Society for Industrial and Applied Mathematics (SIAM), PhiladelphiaGunnels JA, Gustavson FG, Henry GM, van de Geijn RA (2001) FLAME: formal linear algebra methods environment. ACM Trans Math Softw 27(4):422–455. https://doi.org/10.1145/504210.504213Kerekes J, Baum J (2002) Spectral imaging system analytical model for subpixel object detection. IEEE Trans Geosci Remote Sens 40(5):1088–1101León G, González C, Mayo R, Quintana-Ortí ES, Mozos D (2017) Energy-efficient QR factorization on FPGAs. In: Proceedings of 17th International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE 2017), Cádiz, Spai

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

Dimensionality reduction using parallel ICA and its implementation on FPGA in hyperspectral image analysis

Hyperspectral images, although providing abundant information of the object, also bring high computational burden to data processing. This thesis studies the challenging problem of dimensionality reduction in Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension: band selection and feature extraction. This thesis presents a band selection technique based on Independent Component Analysis (ICA), an unsupervised signal separation algorithm. Given only the observations of hyperspectral images, the ICA –based band selection picks the independent bands which contain most of the spectral information of the original images. Due to the high volume of hyperspectral images, ICA -based band selection is a time consuming process. This thesis develops a parallel ICA algorithm which divides the decorrelation process into internal decorrelation and external decorrelation such that computation burden can be distributed from single processor to multiple processors, and the ICA process can be run in a parallel mode. Hardware implementation is always a faster and real -time solution to HSI analysis. Until now, there are few hardware designs for ICA -related processes. This thesis synthesizes the parallel ICA -based band selection on Field Programmable Gate Array (FPGA), which is the best choice for moderate designs and fast implementations. Compared to other design syntheses, the synthesis present in this thesis develops three ICA re-configurable components for the purpose of reusability. In addition, this thesis demonstrates the relationship between the design and the capacity utilization of a single FPGA, then discusses the features of High Performance Reconfigurable Computing (HPRC) to accomodate large capacity and design requirements. Experiments are conducted on three data sets obtained from different sources. Experimental results show the effectiveness of the proposed ICA -based band selection, parallel ICA and its synthesis on FPGA

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

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

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

Advanced Optical Technologies in Food Quality and Waste Management

Food waste is a global problem caused in large part by premature food spoilage. Seafood is especially prone to food waste because it spoils easily. Of the annual 4.7 billion pounds of seafood destined for U.S. markets between 2009 and 2013, 40 to 47 percent ended up as waste. This problem is due in large part to a lack of available technologies to enable rapid, accurate, and reliable valorization of food products from boat or farm to table. Fortunately, recent advancements in spectral sensing technologies and spectroscopic analyses show promise for addressing this problem. Not only could these advancements help to solve hunger issues in impoverished regions of the globe, but they could also benefit the average consumer by enabling intelligent pricing of food products based on projected shelf life. Additional technologies that enforce trust and compliance (e.g., blockchain) could further serve to prevent food fraud by maintaining records of spoilage conditions and other quality validation at all points along the food supply chain and provide improved transparency as regards contract performance and attribution of liability. In this chapter we discuss technologies that have enabled the development of hand-held spectroscopic devices for detecting food spoilage. We also discuss some of the analytical methods used to classify and quantify spoilage based on spectral measurements

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

Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview

Machine learning techniques are widely used for pixel-wise classification of hyperspectral images. These methods can achieve high accuracy, but most of them are computationally intensive models. This poses a problem for their implementation in low-power and embedded systems intended for on-board processing, in which energy consumption and model size are as important as accuracy. With a focus on embedded anci on-board systems (in which only the inference step is performed after an off-line training process), in this paper we provide a comprehensive overview of the inference properties of the most relevant techniques for hyperspectral image classification. For this purpose, we compare the size of the trained models and the operations required during the inference step (which are directly related to the hardware and energy requirements). Our goal is to search for appropriate trade-offs between on-board implementation (such as model size anci energy consumption) anci classification accuracy