nils-herrmann/clean_scirepeval huggingface dataset:
Opto-mechanical microbridles for the determination of structural and functional properties of small resistance arteries

This work presents a polydimethylsiloxane (PDMS) system (named microbridle) monolithically integrating optical (microlenses, air mirrors etc.) and mechanical elements (cantilever) for the real time monitoring of dilatation and contraction events in small resistance arteries. Structural (response to intraluminal pressure changes) and functional properties (response to vasoactive substances) of mesenteric arteries were determined with the microbridle structure and compared to those obtained with conventional myograph systems. Both systems provide comparable data although microbridles were found advantageous in terms of precision, resolution (below the μm) and reliability for the poor contrast between sample and surrounding medium that impede reliable myography recordings.
On the detection of linear mixtures in hyperspectral images

In order to provide reliable information on 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, a new method for fast detection 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 geometrical properties of hyperspectral signatures. Experimental results on synthetic datasets show how the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mix on the scene.
Multisensor Coupled Spectral Unmixing for Time-Series Analysis

We present a new framework, called multisensor coupled spectral unmixing (MuCSUn), that solves unmixing problems involving a set of multisensor time-series spectral images in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems based on regularization on graphs between the time-series data to obtain robust and stable unmixing solutions beyond data modalities due to different sensor characteristics and the effects of nonoptimal atmospheric correction. Atmospheric normalization and cross calibration of spectral response functions are integrated into the framework as a preprocessing step. The proposed methodology is quantitatively validated using a synthetic data set that includes seasonal and trend changes on the surface and the residuals of nonoptimal atmospheric correction. The experiments on the synthetic data set clearly demonstrate the efficacy of MuCSUn and the importance of the preprocessing step. We further apply our methodology to a real time-series data set composed of 11 Hyperion and 22 Landsat-8 images taken over Fukushima, Japan, from 2011 to 2015. The proposed methodology successfully obtains robust and stable unmixing results and clearly visualizes class-specific changes at a subpixel scale in the considered study area.
Spatial Discontinuity-Weighted Sparse Unmixing of Hyperspectral Images

Spectral unmixing is an important technique for remotely sensed hyperspectral image interpretation, of which the goal is to decompose the image into a set of pure spectral components (endmembers) and their abundance fractions in each pixel of the scene. Sparse-representation-based approaches have been widely studied for remotely sensed hyperspectral unmixing. A recent trend is to incorporate the spatial information to improve the spectral unmixing results. Those methods generally assume that the abundances of the pixels are piecewise smooth and fall into a homogeneous region occupied by the same endmembers and their corresponding fractional abundances. However, in real scenarios, abundances may vary abruptly from pixel to pixel. Therefore, the former assumption in most spatial models does not hold. To address this limitation, we propose a new strategy to preserve the spatial details in the abundance maps via a spatial discontinuity weight. Our experimental results, conducted with both simulated and real hyperspectral data sets, illustrate the good potential of our discontinuity-preserving strategy for sparse unmixing, which can greatly improve the abundance estimation results.
Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering

Sparse coding can exploit the intrinsic sparsity of hyperspectral images (HSIs) by representing it as a group of sparse codes. This strategy has been shown to be effective for HSI denoising. However, how to effectively exploit the structural information within the sparse codes (structured sparsity) has not been widely studied. In this paper, we propose a new method for HSI denoising, which uses structured sparse coding and intracluster filtering. First, due to the high spectral correlation, the HSI is represented as a group of sparse codes by projecting each spectral signature onto a given dictionary. Then, we cast the structured sparse coding into a covariance matrix estimation problem. A latent variable-based Bayesian framework is adopted to learn the covariance matrix, the sparse codes, and the noise level simultaneously from noisy observations. Although the considered strategy is able to perform denoising through accurately reconstructing spectral signatures, an inconsistent recovery of sparse codes may corrupt the spectral similarity in each spatial homogeneous cluster within the scene. To address this issue, an intracluster filtering scheme is further employed to restore the spectral similarity in each spatial cluster, which results in better denoising results. Our experimental results, conducted using both simulated and real HSIs, demonstrate that the proposed method outperforms several state-of-the-art denoising methods.
A Hybrid CPU–GPU Real-Time Hyperspectral Unmixing Chain

Hyperspectral images are used in different applications in Earth and space science, and many of these applications exhibit real- or near real-time constraints. A problem when analyzing hyperspectral images is that their spatial resolution is generally not enough to separate different spectrally pure constituents (endmembers); as a result, several of them can be found in the same pixel. Spectral unmixing is an important technique for hyperspectral data exploitation, aimed at finding the spectral signatures of the endmembers and their associated abundance fractions. The development of techniques able to provide unmixing results in real-time is a long desired goal in the hyperspectral imaging community. In this paper, we describe a real-time hyperspectral unmixing chain based on three main steps: 1) estimation of the number of endmembers using the hyperspectral subspace identification with minimum error (HySime); 2) estimation of the spectral signatures of the endmembers using the vertex component analysis (VCA); and 3) unconstrained abundance estimation. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative real-time unmixing chain using graphics processing units (GPUs), exploiting NVIDIA's compute unified device architecture (CUDA) and its basic linear algebra subroutines (CuBLAS) library, as well as OpenMP and BLAS for multicore parallelization. As a result, our real-time chain exploits both CPU (multicore) and GPU paradigms in the optimization. Our experiments reveal that this hybrid GPU-CPU parallel implementation fully meets real-time constraints in hyperspectral imaging applications.
A New Digital Repository for Hyperspectral Imagery With Unmixing-Based Retrieval Functionality Implemented on GPUs

Over the last few years, hyperspectral image data have been collected for a large number of locations over the world, using a variety of instruments for Earth observation. In addition, several new hyperspectral missions will become operational in the near future. Despite the increasing availability and large volume of hyperspectral data in many applications, there is no common repository of hyperspectral data intended to distribute and share free hyperspectral data sets in the community. Quite opposite, the hyperspectral data sets which are available for public use are spread among different storage locations and exhibit significant heterogeneity regarding their format, associated meta-data (if any), or ground-truth information. The development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we take a necessary first step toward the development of a completely open digital repository for remotely sensed hyperspectral data. The proposed system (available online for public use at: http://www.hypercomp.es/repository) allows uploading new hyperspectral data sets along with meta-data, ground-truth, analysis results, and pointers to bibliographic references describing the use of the data. The current implementation consists of a front-end which allows management of hyperspectral images through a web interface. The system is implemented on a parallel cluster system in order to guarantee storage availability and fast performance. The system includes a spectral unmixing-guided content-based image retrieval (CBIR) functionality which allows searching for images from the database using queries or available information such as spectral libraries. Specifically, for each new hyperspectral scene which is cataloged in our system, we extract the spectrally pure components (endmembers) and their associated fractional abundances, and then store this information as metadata associated to the hyperspectral image. The meta-data can be used to efficiently retrieve images based on their information content. In order to accelerate the process of obtaining the metadata for a new entry in the system, we develop efficient implementations of spectral unmixing algorithms on graphics processing units (GPUs). This paper particularly focuses on the software design of the system and provides an experimental validation of the unmixing-based retrieval functionality using both synthetic and real hyperspectral images.
Fast Spatial Preprocessing for Spectral Unmixing of Hyperspectral Data on Graphics Processing Units

Spectral unmixing is an important technique for hyperspectral data exploitation. It amounts at finding a set of pure spectral signatures (endmembers) of the most representative materials in the scene, and estimating their abundance fractions. The integration of spatial information prior to spectral unmixing of hyperspectral data has attracted much attention in recent years. Several approaches have been developed for the purpose of guiding endmember identification algorithms to spatially representative, yet spectrally pure endmembers. In particular, the spatial preprocessing (SPP) algorithm can be used prior to most existing spectral-based endmember identification techniques, thus promoting the selection of endmembers in spatially representative areas of the scene. However, most SPP techniques are computationally expensive, which adds significant burden to the spectral unmixing process. In this paper, we present three parallel implementations of SPP that have been specifically developed for commodity graphics processing units (GPUs). We perform an evaluation of these techniques using two GPU architectures from NVidia: GeForce GTX 580 and GeForce GTX 870M, which reveals that real-time processing performance can be obtained for real hyperspectral data sets collected by the airborne visible infra-red imaging spectrometer (AVIRIS).
Cloud Removal Based on Sparse Representation via Multitemporal Dictionary Learning

Cloud covers, which generally appear in optical remote sensing images, limit the use of collected images in many applications. It is known that removing these cloud effects is a necessary preprocessing step in remote sensing image analysis. In general, auxiliary images need to be used as the reference images to determine the true ground cover underneath cloud-contaminated areas. In this paper, a new cloud removal approach, which is called multitemporal dictionary learning (MDL), is proposed. Dictionaries of the cloudy areas (target data) and the cloud-free areas (reference data) are learned separately in the spectral domain. The removal process is conducted by combining coefficients from the reference image and the dictionary learned from the target image. This method could well recover the data contaminated by thin and thick clouds or cloud shadows. Our experimental results show that the MDL method is effective in removing clouds from both quantitative and qualitative viewpoints.
High-performance computing in remotely sensed hyperspectral imaging: the Pixel Purity Index algorithm as a case study

The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. The price paid for such a wealth of spectral information available from latest-generation sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models in remote sensing missions. This paper explores three HPC-based paradigms for efficient information extraction from remote sensing data using the Pixel Purity Index (PPI) algorithm (available from the popular Kodak's Research Systems ENVI software) as a case study for algorithm optimization. The three considered approaches are: 1) Commodity cluster-based parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGA-based hardware implementations. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC models to remote sensing problems
Parallel Hyperspectral Image and Signal Processing [Applications Corner]

Remotely sensed hyperspectral imaging instruments are capable of collecting hundreds of images corresponding to different wave length channels for the same area on the surface of the Earth. For instance, NASA is continuously gathering high dimensional image data with instruments such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). This advanced sensor for Earth observation records the visible and near-infrared spectrum of the reflected light using more than 200 spectral bands, thus producing a stack of images in which each pixel (vector) is represented by a spectral signal that uniquely characterizes the underlying objects. The resulting data volume typically comprises several gigabytes per flight. In this article, we describe the state of the art in the devel opment and application of image and signal processing techniques for advanced information extraction from hyperspectral data. The article also describes new trends for efficient pro cessing of such data using parallel and distributed processing techniques in the context of time-critical applications.
New bulk accelerometer for triaxial detection

A new structure for a piezoresistive triaxial accelerometer has been designed and fabricated. The FEM simulations shows a null cross sensitivity for the x and y detection and a very low level one for the z direction, <1.6%. The metal lines and the thickness of the passivation silicon oxide layers have been reduced to decrease stresses in the devices. The technology for the devices is a combination of bulk and surface micromachining based on commercial BESOI wafers.
Microneedles electrodes for living cells

This paper shows the technology development of microneedles electrodes for living cell applications. The study includes the fabrication of cantilever electrodes to monitor individual cells. The technology is based on silicon micromachining by combining Deep Reactive Ion Etching and Focused Ion Beam (FIB) nanomachining. Cell biology applications require micrometer size electrodes, smaller than living cells (~20 ¿m), and biocompatible materials. The metal electrode is fabricated in aluminum and passivated by silicon oxide. The opening of the tip electrode by FIB is also performed. Finally, the tip of the electrode is covered by a nanometer thick gold layer deposited by electroless to improved biocompatibility and electrochemical conductivity. The study also addresses questions concerning to the pre-treatment of the aluminum layer before gold deposition in other to improve the adherence and conductivity.
SPT 3.1: A free software for automatic tuning of segmentation parameters in optical, hyperspectral and SAR images

The Segmentation Parameter Tuner (SPT) is a tool designed for automatic tuning of segmentation parameters. In SPT, the goodness of a set of parameter values is given by the level of agreement between the segmentation result and a given reference (representing the desired outcome) quantified by a metric selected by the user (empirical discrepancy methods). This metric is used as the fitness function of an optimization algorithm that searches the parameter space for the minimum value, which is expected to correspond to the segmentation outcome most similar to the reference. SPT 3.1 offers many interesting features such as: five segmentation algorithms (for Optical, Hyperspectral and SAR images), four optimization algorithms (stochastic and direct search optimization methods) and seven discrepancy metrics (pixel and object-based). This paper describes the optimization procedure underlying SPT 3.1, the features added to this version as well as an experiment that illustrates the operation of the tool.
Recent developments and future directions in parallel processing of remotely sensed hyperspectral images

Remotely sensed hyperspectral imaging is a technique that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. Computationally effective processing of these image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, or defense/security. With the aim of providing an overview of recent developments and new trends in the design of parallel and distributed systems for hyperspectral image analysis, this paper discusses and inter-compares four different strategies for efficiently implementing a standard hyperspectral image processing chain: 1) commodity Beowulf-type clusters, 2) heterogeneous networks of workstations, 3) field programmable gate arrays (FPGAs), and 4) graphics processing units (GPUs). Combined, these parts deliver a snapshot of the state-of-the-art in those areas, and a thoughtful perspective on the potential and emerging challenges of adapting high performance computing systems to remote sensing problems.
Spectral Mixture Analysis of Hyperspectral Scenes Using Intelligently Selected Training Samples

In this letter, we address the use of artificial neural networks for spectral mixture analysis of hyperspectral scenes. We specifically focus on the issue of how to effectively train neural network architectures in the context of spectral mixture analysis applications. To address this issue, a multilayer perceptron neural architecture is combined with techniques for intelligent selection and labeling of training samples directly obtained from the input data, thus maximizing the information that can be obtained from those samples while reducing the need for a priori information about the scene. The proposed approach is compared to unconstrained and fully constrained linear mixture models using hyperspectral data sets acquired (in the laboratory) from artificial forest scenes, using the compact airborne spectrographic imaging system. The Spreading of Photons for Radiation INTerception (SPRINT) canopy model, which assumes detailed knowledge about object geometry, was employed to evaluate the results obtained by the different methods. Our results show that the proposed approach, when trained with both pure and mixed training samples (generated automatically without prior information) can provide similar results to those provided by SPRINT, using very few labeled training samples. An application to real airborne data using a set of hyperspectral images collected at different altitudes by the digital airborne imaging spectrometer 7915 and the reflective optics system imaging spectrometer, operating simultaneously at multiple spatial resolutions, is also presented and discussed.
GPU implementation of spatial preprocessing for spectral unmixing of hyperspectral data

The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics of the hyperspectral data. Particularly, spatial preprocessing algorithm (SPP) consists on a preprocessing technique that can be used prior to most of existing spectral-based endmember extraction process, thus promoting the selection of endmem-bers from the most spatially homogeneous regions of the data set. This paper presents a parallel implementation of SPP algorithm which is tested over two different graphic processing units (GPUs) architectures: NVidiaTMGeForce GTX 580 and NVidiaTMGeForce GTX 870M. Experimental validation using a hyperspectral data set collected by AVIRIS sensor shows that it is possible to achieve real-time performance.
Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach

Hyperspectral imaging is a widely used technique in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the earth. In the last two decades, several methods (unsupervised, supervised, and semisupervised) have been proposed to deal with the hyperspectral image classification problem. Supervised techniques have been generally more popular, despite the fact that it is difficult to collect labeled samples in real scenarios. In particular, deep neural networks, such as convolutional neural networks (CNNs), have recently shown a great potential to yield high performance in the hyperspectral image classification. However, these techniques require sufficient labeled samples in order to perform properly and generalize well. Obtaining labeled data is expensive and time consuming, and the high dimensionality of hyperspectral data makes it difficult to design classifiers based on limited samples (for instance, CNNs overfit quickly with small training sets). Active learning (AL) can deal with this problem by training the model with a small set of labeled samples that is reinforced by the acquisition of new unlabeled samples. In this paper, we develop a new AL-guided classification model that exploits both the spectral information and the spatial-contextual information in the hyperspectral data. The proposed model makes use of recently developed Bayesian CNNs. Our newly developed technique provides robust classification results when compared with other state-of-the-art techniques for hyperspectral image classification.