nils-herrmann/clean_scirepeval huggingface dataset:
Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data

Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Xiaoli, also called RX algorithm. A variation of this algorithm consists of applying the same concept to a local sliding window centered around each image pixel. The computational cost is very high for RX algorithm and it strongly increases for its local versions. However, current advances in high performance computing help to reduce the run-time of these algorithms. So, for the standard RX, it is possible to achieve a processing time similar to the data acquisition time and to increase the practical interest for its local versions. In this paper, we discuss several optimizations which exploit different forms of acceleration for these algorithms. First, we explain how the calculation of the correlation matrix and its inverse can be accelerated through optimization techniques based on the properties of these particular matrices and the efficient use of linear algebra libraries. Second, we describe parallel implementations of the RX algorithm, optimized for multicore platforms. These are well-known, inexpensive and widely available high performance computing platforms. The ability to detect anomalies of the global and local versions of RX is explored using a wide set of experiments, using both synthetic and real data, which are used for comparing the optimized versions of the global and local RX algorithms in terms of anomaly detection accuracy and computational efficiency. The synthetic images have been generated under different noise conditions and anomalous features. The two real scenes used in the experiments are a hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and another data set collected by the HYperspectral Digital Image Collection Experiment (HYDICE). Experimental results indicate that the proposed optimizations can significantly improve the performance of the considered algorithms without reducing their anomaly detection accuracy.
Spectrometer-Driven Spectral Partitioning for Hyperspectral Image Classification

Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for hyperspectral image classification assume that the spectral signatures provided by an imaging spectrometer can be interpreted as a unique and continuous signal, in practice, this signal may be obtained after the combination of several individual responses obtained from different spectrometers. In this work, we propose a new spectral partitioning strategy prior to classification which takes into account the physical design of the imaging spectrometer system for partitioning the spectral bands collected by each spectrometer, and resampling them into different groups or partitions. The final classification result is obtained as a combination of the results obtained from each individual partition by means of a multiple classifier system (MCS). The proposed strategy not only incorporates the design of the imaging spectrometer into the classification process but also circumvents problems such as the curse of dimensionality given by the unbalance between the high number of spectral bands and the generally limited number of training samples available for classification purposes. This concept is illustrated in this work using two different imaging spectrometers: the airborne visible infra-red imaging spectrometer, operated by NASA, and the digital airborne imaging system (DAIS), operated by the German Aerospace Center. Our experiments indicate that the proposed spectral partitioning strategy can lead to classification improvements on the order of 5% overall accuracy when using state-of-the-art spatial–spectral classifiers with very limited training samples.
BESOI-based integrated optical silicon accelerometer

The design, simulation, fabrication and characterization of a new integrated optical accelerometer is presented in this paper. The reduction of fabrication, packaging and thermomechanical stresses are considered by keeping the weak mechanical parts free of stresses. The mechanical sensor consists on a quad beam structure with one single mass. In addition, there are two waveguides on the frame of the chip self-aligned to one on the mass of the accelerometer. Four lateral beams increase the mechanical sensitivity and allow the flat displacement of the optical waveguides on the mass. The working principle is based on the variation of the output light intensity versus the acceleration due to the misalignment of the waveguides. The devices have been optimized by the finite-element method to obtain a mechanical sensitivity of 1 /spl mu/m/g. The fabrication technology is based on BESOI wafers combining bulk an surface micromachining. Moreover, machined glass wafers with cavities are bonded to the silicon wafer for packaging and damping control. Special packaging considerations as dicing, polishing and alignment are also presented. Optical measurements at 633 nm shown an optical sensitivity of 2.3 dB/g for negative and 1.7 dB/g for positive acceleration. This difference in the sensitivity has been demonstrated as a consequence of the passivation layer located over the core of the waveguides.
Parallel Implementation of Polarimetric Synthetic Aperture Radar Data Processing for Unsupervised Classification Using the Complex Wishart Classifier

This work investigates the parallel implementation of target decomposition and unsupervised classification algorithms for polarimetric synthetic aperture radar (POLSAR) data processing. The algorithms are implemented using two different parallel programming models: 1) clusters of CPUs, using message passing interface (MPI), and 2) commodity graphic processing units (GPUs), using the compute device unified architecture (CUDA). POLSAR data processing generally involves a large amount of computations as the full polarimetric information needs to be decomposed and analyzed. Our experiments reveal that GPU architectures provide a good framework for massive parallelization of POLSAR data processing. For instance, it is found that a single GPU can be more efficient than a cluster of 128 nodes with speedups of more than $100 \times $ in comparison with the single processor times. The proposed implementation makes the best use of low-level features in the GPU architecture such as shared memories, while also providing coalesced accesses to memory in order to achieve maximum performance.
A New Digital Repository for Remotely Sensed Hyperspectral Imagery on GPUs

Hyperspectral imaging is a new technique in remote sensing in which an imaging spectrometer collects hundred of images (at different wavelength channels) for the same area on the surface of Earth. Over the last years, hyperspectral image data sets have been collected from a great amount of locations over the world using a variety of instruments for Earth observation. Only a small amount of them are available for public use and they are spread among different storage locations and exhibit significant heterogeneity regarding the storage format. Therefore, the development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we describe the development of a shared digital repository for remotely sensed hyperspectral data, which allows uploading new hyperspectral data sets along with meta-data, ground-truth and analysis results (spectral information). Such repository is presented as a web service for providing the management of images through a web interface, and it is available online from http://www.hypercomp.es/repository. Most importantly, the developed system includes a spectral unmixing-based content based image retrieval (CBIR) functionality which allows searching for images from the database using spectrally pure components or endmembers in the scene. A full spectral unmixing chain is implemented for spectral information extraction, which allows filtering images using the similarity of the spectral signature and abundance of a given ground-truth. In order to accelerate the process of obtaining the spectral information for new entries in the system, we resort to an efficient implementations of spectral unmixing algorithms of graphics processing units (GPUs).
GPU Implementation of Composite Kernels for Hyperspectral Image Classification

In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial-spectral HSI classification for the first time in the literature.
Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis

This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches.
Recent activities in the Hyperspectral Imaging Network (HYPER-I-NET): A European consortium fostering imaging spectroscopy research

The Hyperspectral Imaging Network (HYPER-I-NET) is a four-year Marie Curie Research Training Network project designed to build an interdisciplinary European research community focusing on hyperspectral imaging activities. The network is currently formed by a multidisciplinary team composed of fifteen highly experienced European partner organizations. In this paper, we outline the activities that have been carried out in the four main areas covered by HYPER-I-NET: 1) hyperspectral sensor specification, 2) processing chain definition and implementation, 3) calibration, validation and definition of standardization mechanisms, and 4) science applications. Along with the description of the progress made in the four main areas listed above, this paper also describes some of the training and transfer of knowledge activities carried out during the first two years of the project.
Parallel Morphological Classification of Hyperspectral Imagery Using Extended Opening and Closing by Reconstruction Operations

Hyperspectral image processing has been a very active area in remote sensing and other application domains in recent years. Despite the availability of a wide range of advanced processing techniques for hyperspectral data analysis, many techniques for hyperspectral data classification are based on the consideration of spectral information separately from spatial information information, and thus the two types of information are not treated simultaneously. In this paper, we develop a new technique for joint spatial-spectral classification of hyperspectral image data which makes use of opening and closing by reconstruction, a kind of mathematical morphology operations which are extended here to hyperspectral images. A high performance parallel implementation of the proposed technique is also developed to satisfy time-critical constraints in remote sensing applications, using NASA's Thunderhead Beowulf cluster computer for demonstration purposes.
Integrating multiple nonlinear estimators into hyperspectral unmixing

Linear spectral unmixing has been widely used for hyperspectral data interpretation. However, there is a need for nonlinear unmixing methods that can model more complex geometries without the need to resort to prior knowledge about the objects in the scene. In this paper, we present a novel strategy for nonlinear spectral unmixing which combines polytope decomposition (POD) with artificial neural network (ANN)-based learning. Even if no ground-truth information is available, the ANN can still efficiently estimate the order of the nonlinearity involved in the problem and enhance the capacity of the POD method to deliver unmixing performance for a wider range of nonlinearities. The proposed method has been evaluated using both simulated and real scenes, providing promising results.
Commodity Cluster-Based Parallel Implementation of an Automatic Target Generation Process for Hyperspectral Image Analysis

The development of parallel implementations is an important task for hyper spectral data exploitation. In most cases, real-time or nearly real-time processing of hyper spectral images is required for swift decisions which depend upon high computing performance of algorithm analysis. A popular algorithm in hyper spectral image interpretation is the automatic target generation process (ATGP). In this paper, we develop a new parallel version of this algorithm, which is routinely applied in many application domains, including defence and intelligence, precision agriculture, geology, or forestry. We improve considerably the computational cost of this algorithm, and also improve its detection accuracy by incorporating a new method for calculating the orthogonal projection process in which the algorithm is based using the Gram-Schmidt method. Our proposed strategy reduces the computational cost over the a previous implementation of the same algorithm which uses the pseudoinverse operation to compute the orthogonal projection. Our parallel algorithm is implemented on a multi-core cluster system made up of of sixteen nodes, with two CPUs of four cores per node, and quantitatively evaluated using hyper spectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) in New York and over the Cup rite mining district, Nevada, United States.
Remote Sensing Image Classification Using Attribute Filters Defined Over the Tree of Shapes

Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of min, max, direct, and subtractive filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas.
Efficient information extraction from hyperspectral imagery using networks of workstations

The rapid development in space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particular, NASA is continuously gathering imagery data with hyperspectral sensors such as the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) or the Hyperion imager aboard Earth Observing-1 (EO-1) spacecraft. The development of efficient techniques for transforming the massive amount of collected data into scientific understanding is critical for space-based Earth science and planetary exploration. Heterogeneous networks of workstations are a very promising cost-effective parallel computing architecture. Unlike traditional homogeneous parallel platforms, heterogeneous architectures are composed of processors running at different speeds. This heterogeneity results in distributed-memory parallel computing systems created from commodity components that can satisfy specific computational requirements for the Earth and space sciences community. This paper explores techniques for mapping hyperspectral image analysis algorithms onto heterogeneous networks of workstations. Important aspects in algorithm design such as portability, reusability and scalability are illustrated by using homogeneous and heterogeneous parallel computing facilities at NASA’s Goddard Space Flight Center and, European Center for Parallelism of Barcelona, and University of Extremadura in Spain. Hyperspectral image data from the AVIRIS data repository is used in experiments, which reveal that heterogeneous networks of workstations are a source of computational power that is both accessible and applicable to obtaining results quickly enough for practical use in information extraction applications from hyperspectral imagery. KeywordsHyperspectral imaging, Parallel algorithms, Heterogeneous computing, Spatial/spectral analysis.
Region-Based Spatial Preprocessing for Endmember Extraction and Spectral Unmixing

Linear spectral unmixing is an important task in remotely sensed hyperspectral data exploitation. This approach first identifies a collection of spectrally pure constituent spectra, called endmembers, and then expresses the measured spectrum of each mixed pixel as a combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember in the pixel. Over the last decade, several algorithms have been developed for automatic extraction of spectral endmembers from hyperspectral data, with many of them relying exclusively on the spectral information. In this letter, we develop a novel unsupervised spatial preprocessing (SPP) module which adopts a region-based approach for the characterization of each endmember class prior to endmember identification using spectral information. The proposed approach can be combined with any spectral-based endmember extraction technique. Our method is validated using both synthetic scenes constructed using fractals and a real hyperspectral data set collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite Mining District in Nevada and further compared with previous efforts in the same direction such as the spatial-spectral endmember extraction, automatic morphological endmember extraction, or SPP methods.
Class-oriented spectral partitioning for hyperspectral image classification

This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.
Parallel implementation of a hyperspectral unmixing chain: Graphic processing units versus multi-core processors

Spectral unmixing is an important tast for remotely sensed hyperspectral imaging. Spectral unmixing algorithms can be computationally expensive. In this paper, we develop two efficient implementations of a full hyperspectral unmixing chain on two different kinds of high performance computing architectures: graphics processing units (GPUs) and multi-core processors. These platforms are inter-compared in the context of hyperspectral unmixing. Our experimental results reveal that both GPUs and multi-core processors can provide real-time unmixing performance when properly implemented in this kind of parallel platforms.
FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis

Hyperspectral remote sensing attempts to identify features in the surface of the Earth using sensors that generally provide large amounts of data. The data are usually collected by a satellite or an airborne instrument and sent to a ground station that processes it. The main bottleneck of this approach is the (often reduced) bandwidth connection between the satellite and the station, which drastically limits the information that can be sent and processed in real time. A possible way to overcome this problem is to include onboard computing resources able to preprocess the data, reducing its size by orders of magnitude. Reconfigurable field-programmable gate arrays (FPGAs) are a promising platform that allows hardware/software codesign and the potential to provide powerful onboard computing capability and flexibility at the same time. Since FPGAs can implement custom hardware solutions, they can reach very high performance levels. Moreover, using run-time reconfiguration, the functionality of the FPGA can be updated at run time as many times as needed to perform different computations. Hence, the FPGA can be reused for several applications reducing the number of computing resources needed. One of the most popular and widely used techniques for analyzing hyperspectral data is linear spectral unmixing, which relies on the identification of pure spectral signatures via a so-called endmember extraction algorithm. In this paper, we present the first FPGA design for N-FINDR, a widely used endmember extraction algorithm in the literature. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-4 XC4VFX60 FPGA (a model that is similar to radiation-hardened FPGAs certified for space operation) and tested using real hyperspectral data collected by NASA's Earth Observing-1 Hyperion (a satellite instrument) and the Airborne Visible Infra-Red Imaging Spectrometer over the Cuprite mining district in Nevada and the Jasper Ridge Biological Preserve in California. Experimental results demonstrate that our hardware version of the N-FINDR algorithm can significantly outperform an equivalent software version and is able to provide accurate results in near real time, which makes our reconfigurable system appealing for onboard hyperspectral data processing.