nils-herrmann/clean_scirepeval huggingface dataset:
Real-time spectral unmixing using iterative error analysis on commodity graphics processing units

Spectral unmixing is an important task for hyperspectral data exploitation. It generally consists of two steps: identification of pure spectral signatures (endmembers) and estimation of the fractional abundance of each endmember in each pixel of the scene. A successful algorithm to perform both tasks in simultaneous fashio is the iterative error analysis (IEA) algorithm, which applies an iterative process in which the next endmember to be detected depends on the set of previously extracted ones, which can be computationally expensive for hyperspectral images with a large number of endmembers. In this paper, we propose a new parallel implementation of the IEA algorithm for graphics processing units (GPUs). The proposed implementation is tested on three different GPUs fromNVidia™, and is shown to exhibit real-time performance in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data set collected over the Cuprite mining district in Nevada.
Parallel Implementation of a Full Hyperspectral Unmixing Chain Using OpenCL

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral unmixing faces important challenges in order to characterize mixed pixels. As a result, several hyperspectral unmixing chains have been proposed to find the spectral signatures for each endmember and their associated abundance fractions. However, unmixing algorithms can be computationally expensive, which compromises their use in applications under real-time constraints. In this paper, we describe a new parallel hyperspectral unmixing chain based on three stages: 1) estimation of the number of endmembers using the geometry-based estimation of number of endmembers algorithm; 2) automatic identification of the spectral signatures of the endmembers using the simplex growing algorithm; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene using the sum-to-one constrained least-squares unmixing algorithm. These algorithms have been specifically selected due to their successful performance in different applications. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative unmixing chain using an hybrid implementation with the OpenCL framework and clMAGMA library. As a result, this is one of the first real-time implementations of a full unmixing chain in an open computing language. This methodology can be executed on different heterogeneous platforms such as CPU (multicore) and GPU platforms, in which accuracy, performance, and power consumption terms have been considered.
Dose-Free Monitoring of Radiotherapy Treatments With Scattered Photons: First Experimental Results at a 6-MV Linac

Radiotherapy (RT) is nowadays, after surgery the most frequently used treatment of cancer. For predicted clinical outcomes it is of importance assuring that the treatment plan is correctly delivered without dose deviations, namely target underdosage and/or organ-at-risk overdosage. For that, a new multipixel imaging technique for real-time dose verification during photon RT has been proposed (RTmon). The principle of operation of RTmon relies on the detection of scattered photons emitted perpendicularly to the beam direction. First scanned, single-pixel experimental results are presented. These results were obtained with a collimated γ-ray detector operated under a 6-MV clinical linac beam. A large contaminating background originating from the head of the linac was found. We show that its mitigation is possible by (1) using appropriate shielding, (2) decreasing the scintillator volume pointing towards out-of-field, background-prone regions, (3) optimizing the collimation by means of detector positioning, and (4) applying background subtraction techniques. Simulation and experimental data show that multiple scattering inside a phantom target constitutes an additional source of background that increases along the beam direction due to momentum conservation in collisions between the incoming photons and target electrons. This second source of background can be reduced effectively by optimizing the distance between the collimated detector and the beam axis, at the expense of decreased collimation sensitivity. Despite the harsh therapeutic irradiation conditions, background and noise suppression by appropriate techniques renders the scanned experimental results quite similar to the dose obtained by means of Geant4 simulations. Such experimental results suggest that this detection system may be useful for non-invasive, real-time, in-situ dose verification in external X-ray beam radiotherapy.
Parallel Spatial–Spectral Hyperspectral Image Classification With Sparse Representation and Markov Random Fields on GPUs

Spatial-spectral classification is a very important topic in the field of remotely sensed hyperspectral imaging. In this work, we develop a parallel implementation of a novel supervised spectral-spatial classifier, which models the likelihood probability via l1 - l2 sparse representation and the spatial prior as a Gibbs distribution. This classifier takes advantage of the spatial piecewise smoothness and correlation of neighboring pixels in the spatial domain, but its computational complexity is very high which makes its application to time-critical scenarios quite limited. In order to improve the computational efficiency of the algorithm, we optimized its serial version and developed a parallel implementation for commodity graphics processing units (GPUs). Our parallel spatial-spectral classifier with sparse representation and Markov random fields (SSC-SRMRF-P) exploits the low-level architecture of GPUs. The parallel optimization of the proposed method has been carried out using the compute unified device architecture (CUDA). The performance of the parallel implementation is evaluated and compared with the serial and multicore implementations on central processing units (CPUs). In fact, the proposed method has been designed to adequately exploit the massive data parallel capacities of GPUs together with the control and logic capacities of CPUs, thus resorting to a heterogeneous CPU-GPU framework in the design of the parallel algorithm. Experimental results using real hyperspectral images demonstrate very high performance for the proposed CPU-GPU parallel method, both in terms of classification accuracy and computational performance.
Spatial technology and social media in remote sensing: challenges and opportunities [point of view]

The convergence of remote sensing technologies with social media, coupled with advances in other location-aware technologies such as WiFi and smartphones, is moving us on a fast track to a situation in which we can readily know, at any time, where everybody and everything are located on the surface of the Earth, and to exploit the power of social media in different contexts. Remote sensing technology involves the use of systems and algorithms to record information about the surface of the Earth from a remote location  . Although reliable as a data source, remote sensing data may not always be available. However, these data can be complemented by other sources of data, such as geographic information systems (GIS) and social media  , in order to address time-critical applications. For instance, relating publicly available social media information with remote sensing or GIS data can lead to a more efficient management of emergency response (which refers to applications in which real/near-real-time response is needed, such as natural disasters). Social media are now playing a more relevant role in our daily lives and provide a unique opportunity to gain valuable insight on information flow and social networking within the society. As a result, the integration of social media data with other consolidated technologies such as remote sensing or GIS is of great importance.
Integrated polymer optical accelerometer

A quad-beam polymer optical accelerometer, based in the modulation of the total losses as a function of the acceleration, is presented in this letter. Three waveguides are defined on the structure: two at the edges and the third in the middle of a movable seismic mass suspended by polymer springs. A displacement of the mass, caused by acceleration, increases the losses due to the misalignment between the waveguides. Optical simulations predict an extremely high optical sensitivity of 11 dB/g. The novelty of the proposed device is the use of polymer as a mechanical/optical material. The sensitivity of the accelerometer has been measured to be at least 6 dB/g, much higher than any previously reported optical accelerometer based on the same operation principle. These results confirm the validity of the proposed high-sensitivity low-cost polymer accelerometer.
Magnetically-actuated variable optical attenuators using ferrofluid-doped elastomer implemented by combination of soft lithography and inkjet printing technologies

This paper reports the implementation of magnetic variable optical attenuators (M-VOA) by soft lithography (SLT) and using polydimethylsiloxane (PDMS) as constituent material. Two different fabrication protocols are used and compared. In the first case, a two-layer structure containing a clean PDMS layer on a magnetic PDMS (M-PDMS) layer is fabricated by SLT. M-PDMS is obtained by doping clean PDMS with different ferrofluid (FF) amounts. The second protocol consists of selectively dispensing droplets of FF by the inkjet printing technique (IPT) on a clean and non-cured PDMS structure previously defined by SLT. The optical and mechanical properties of structures fabricated using both protocols and containing similar ferrofluid amounts are compared.
A Novel Preunmixing Framework for Efficient Detection of Linear Mixtures in Hyperspectral Images

In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of the geometrical properties of hyperspectral signatures. Experimental results on both real and synthetic data sets show that the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mixtures present in the pixels of the scene.
Parallel hyperspectral image compression using iterative error analysis on graphics processing units

In this paper, we develop a new parallel implementation of the iterative error analysis (IEA) algorithm for lossy hyperspectral image compression on graphics processing units (GPUs), an inexpensive parallel computing platform that has recently become very popular in hyperspectral imaging applications. The proposed GPU implementation is tested on several different architectures from NVidia, the main GPU vendor worldwide, and is shown to exhibit real-time performance in the analysis of AVIRIS data sets. The GPU implementation of the IEA represents a step forward towards real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also adjusted in real-time.
Cluster-Based Implementation of a Morphological Watershed Algorithm for Parallel Classification of Multichannel Images

Due to the large data volumes often associated with multichannel imagery in many application domains, including satellite imaging and aerial reconnaissance, there is a clear need for parallel algorithms able to process these high-dimensional data sets quickly enough for practical use. This paper describes a parallel implementation of a multichannel classification algorithm that naturally combines the information provided by all dimensions. The parallel algorithm first defines multichannel morphological operations, and then uses these operations to extend the morphological watershed transformation to multidimensional images. The proposed technique has been implemented on Thunder-head, a Beowulf cluster at NASA's Goddard Space Flight Center, and tested using remotely sensed data collected by the Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).
Harmonic Mixture Modeling for Efficient Nonlinear Hyperspectral Unmixing

Higher order nonlinear material mixtures provide a good model to explain the effects of physical-chemical phenomena on hyperspectral remote sensing measurements. Therefore, inverting nonlinear effects starting from the measured spectral values is a very challenging yet fundamental task to provide a thorough and reliable characterization of the materials in a scene. In this paper, this task is achieved by inverting a new model for nonlinear hyperspectral mixtures. Specifically, we show that it is possible to effectively unmix hyperspectral data by assuming a harmonic description of the higher order nonlinear combination of the endmembers. The rationale for this model is that the harmonic analysis is able to understand and quantify effects that cannot be effectively described by classic polynomial combinations. Although the model is nonlinear, unmixing is performed by solving a linear system thanks to the recently proposed polytope decomposition (POD). Experimental results show that inverting this model leads to improved performances with respect to the state of the art in terms of endmember abundance estimation both over synthetic and real datasets.
GPU implementation of hyperspectral image classification based on weighted Markov random fields

The dimensionality of hyperspectral data is very high, and spectral-spatial hyperspectral classification techniques are quite demanding from a computational viewpoint. In this paper, we present a computationally efficient implementation of a spectral-spatial classification method based on weighted Markov random fields. The method learns the spectral information from a sparse multinomial logistic regression (SMLR) classifier, and the spatial information is characterized by modeling the potential function associated with a weighted Markov random field (MRF) as a spatially adaptive vector total variation function. The parallel implementation has been carried out using commodity graphics processing units (GPUs) and the NVIDIA's compute unified device architecture (CUDA), thus exploiting the massively parallel nature of GPUs to achieve significant acceleration factors with regards to the serial version of the same classifier on an NVIDIA Tesla C2075 platform.
Spatial Technology and Social Media in Remote Sensing: A Survey

The rapid development of social media data and the associated growth in volume, velocity, and variety has fostered the idea of using these data to guide traditional remote sensing image retrieval and information extraction tasks. Although important progress has been made in recent years in harvesting spatial and temporal data from social media, the exploitation of these data for decision making still needs further investigation, particularly in the context of its integration with remote sensing and geographic information systems. In this paper, we first discuss the relation between localization techniques and spatial technologies, pointing out their similarities and differences. Then, we provide a discussion on location analysis of social media data, and the fusion of multiple data sources, with specific attention to the integration of social media content (including localization) with remote sensing-based spatial technologies. Next, we provide specific examples addressing the use of social media data to perform information extraction from large remote sensing data repositories. Although significant possibilities for the integration of localization and spatial technologies can be seen in the examples provided, our survey suggests that the convergence of remote sensing and social media data will continue to deeply transform these technologies.
Class-Oriented Spectral Partitioning for Remotely Sensed Hyperspectral Image Classification

Remotely sensed hyperspectral images exhibit very high dimensionality in the spectral domain. As opposed to band selection techniques, which extract a subset of the original spectral bands in the image, spectral partitioning (SP) techniques reassign the original bands into subgroups that are then processed separately. From a classification perspective, this strategy has the advantage that all the original information in the hyperspectral data can be retained while addressing the curse of dimensionality given by the Hughes phenomenon. Even if SP prior to classification has been widely used, the strategies adopted to perform such partitioning did not consider the diversity of spectral classes in the scene. In other words, available techniques are not driven by the information contained in the classes of interest, which can be very useful to perform the SP in a more effective manner for classification purposes. To address this issue, in this paper, we present a new class-oriented SP technique that exploits prior information about the classes by automatically ranking the spectral bands that are more useful for each specific class (instead of considering the hyperspectral image as a whole). The resulting multiple subgroups of bands with lower dimensionality are then fed to a multiple classifier system. Our experimental results, conducted with three different hyperspectral airborne images, suggest that the presented method leads to competitive results when compared to other state-of-the-art approaches in the field.
Cantilever-based poly(dimethylsiloxane) Microoptoelectromechanical Systems

Poly(dimethylsiloxane) (PDMS) has been considered for the first time for defining cantilever-based microoptoelectromechanical systems (MOEMS) by ways of soft lithography. The systems shown have a high degree of integration, comprising cantilever-waveguides, lenses, self alignment systems and seismic mass. The very low Young's modulus allows defining thick PDMS-MOEMS for mechanical sensors with sensitivities similar to its nanometer scale silicon counterparts. Experimental resonant frequencies of the proposed MOEMS are in agreement with the numerical simulations done, with Q factors in agreement with the PDMS properties. Finally, when a 1.58 mg droplet of ethanol is dispensed on the cantilever, there is a bending of the cantilever, causing an increase of the relative losses until 25 dB, returning to its initial value when the droplet evaporates.
Numerical simulation of SU-8 optical accelerometers

This contribution deals with the optimization of a new SU-8 optical accelerometer. Microaccelerometers became large-scale production devices the last 90's. Several working principles have been applied for acceleration sensing. However, most of them suffer from high cross-sensitivity to electromagnetic interferences (EMI) and they cannot be used under harsh/explosive atmospheres. Therefore, optical accelerometers can be used to avoid these drawbacks. We introduced a new integrated polymer optical accelerometer based on intensity modulation formed by a SU-8 polymer structure. The SU-8 polymer is used as structural material but also as optical waveguide. It consists of one mass adjoined to four beams, all of them made of SU-8. There are three aligned optical waveguides: one on the mass and two fixed to the substrate. Any applied acceleration will misalign the three waveguides; the losses show the acceleration level. The paper is focused on the numerical simulation of this device. It is a one-way sequentially coupled analysis. Firstly, a finite element method (FEM) simulation is done to calculate the mechanical sensitivity of the structure. In parallel, the beam profile at the waveguides is computed by using the non-uniform finite difference method (NU-FDM). Finally, the evolution of the light beam in the propagating axis of the waveguides is simulated using the beam propagation method (BPM) by introducing the results of the previous simulations as loads. The good agreement between the experimental results and the simulation demonstrates the feasibility of the sequential couple of the mechanical and optical fields by using the three different numerical methods
A CMOS monolithically integrated gas sensor array with electronics for temperature control and signal interfacing

A monolithically integrated gas sensor array with conventional CMOS electronics has been developed. Circuitry includes the heater driver for the temperature control and the signal interface electronics for inspection of the four sensors in the array. Because of its characteristics as robustness, small, size, low price and future capabilities with the adequate signal processing the system would be able to cover most of today's markets considered for gas sensors.
A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantially different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied.
Parallel morphological processing of hyperspectral image data on heterogeneous networks of computers

Advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. 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. Although most currently available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent a very promising cost-effective solution expected to play a major role in the design of high-performance computing platforms for many on-going and planned remote sensing missions. This paper explores techniques for mapping morphological hyperspectral analysis algorithms, characterized by their scalability and sub-pixel accuracy, onto heterogeneous parallel computers. Important aspects in algorithm design are illustrated by using both homogeneous and heterogeneous parallel computing facilities available at NASA's Goddard Space Flight Center and University of Maryland. Experiments reveal that heterogeneous networks of workstations represent a source of computational power that is both accessible and applicable in many remote sensing studies