nils-herrmann/clean_scirepeval huggingface dataset:
Real-Time Identification of Hyperspectral Subspaces

The identification of signal subspace is a crucial operation in hyperspectral imagery, enabling a correct dimensionality reduction that often yields gains in algorithm performance and efficiency. This paper presents new parallel implementations of a widely used hyperspectral subspace identification with minimum error (HySime) algorithm on different types of high-performance computing architectures, including general purpose multicore CPUs, graphics processing units (GPUs), and digital signal processors (DSPs). We first developed an optimized serial version of the HySime algorithm using the C programming language, and then we developed three parallel versions: one for a multi-core Intel CPU using the OpenMP API and the ATLAS algebra library, another one using NVIDIA's compute unified device architecture (CUDA) and its basic linear algebra subroutines library (CuBLAS), and another one using a Texas' multicore DSP. Experimental results, based on the processing of simulated and real hyperspectral images of various sizes, show the effectiveness of our GPU and multicore CPU implementations, which satisfy the real-time constraints given by the data acquisition rate. The DSP implementation offers a good tradeoff between low power consumption and computational performance, but it is still penalized by the absence of double precision floating point accuracy and/or suitable mathematical libraries.
Big Data for Remote Sensing: Challenges and Opportunities

Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.
A Web-Based System for Classification of Remote Sensing Data

The availability of satellite imagery has expanded over the past few years, and the possibility to perform fast processing of massive databases comprising this kind of imagery data has opened ground-breaking perspectives in many different fields. This paper describes a web-based system (available online: http://hypergim.ceta-ciemat.es), which allows an inexperienced user to perform unsupervised classification of satellite/airborne images. The processing chain adopted in this work has been implemented in C language and integrated in our proposed tool, developed with HTML5, JavaScript, Php, AJAX and other web programming languages. Image acquisition with the applications programmer interface (API) is fast and efficient. An important added functionality of the developed tool is its capacity to exploit a remote server to speed up the processing of large satellite/airborne images at different zoom levels. The ability to process images at different zoom levels allows the tool an improved interaction with the user, who is able to supervise the final result. The previous functionalities are necessary to use efficient techniques for the classification of images and the incorporation of content-based image retrieval (CBIR). Several experimental validation types of the classification results with the proposed system are performed by comparing the classification accuracy of the proposed chain by means of techniques available in the well-known Environment for Visualizing Images (ENVI) software package.
Semi-supervised classification of urban hyperspectral data using spectral unmixing concepts

Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, possible connections between semi-supervised classification and spectral unmixing concepts have been rarely investigated. In this work, we propose a new method to perform semi-supervised classification of urban hyperspectral images by exploiting the information retrieved with spectral unmixing. The proposed approach integrates a well-established discriminative classifier (multinomial logistic regression) with two different spectral unmixing chains, thus bridging the gap between unmixing and classification. Moreover, the proposed method uses active learning when generating new unlabeled samples for classification.
Fusion of hyperspectral and LiDAR data using morphological component analysis

This paper presents a new classification framework for the fusion of hyperspectral and LiDAR data. The proposed approach aims at exploiting the complementarity of the features, i.e., textural features in the hyperspectral data and the height features in the LiDAR data, respectively. In this work, we use a morphological component analysis (MCA) method for textural feature extraction. The classification is then executed by a multinomial logistic regression classifier (MLR). Our obtained experimental results reveal that the proposed feature fusion method can lead to very good classification results.
Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion

Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover categorization. Then, a novel pLSA-based image fusion approach is presented, which pursues to uncover multimodal feature patterns from SAR and MSI data in order to effectively fuse and categorize Sentinel-1 and Sentinel-2 remotely sensed data. Experiments conducted over two different data sets reveal the advantages of the proposed approach for unsupervised land-cover categorization tasks.
Parallel CBIR System for Efficient Hyperspectral Image Retrieval from Heterogeneous Networks of Workstations

The purpose of content-based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. In remote sensing applications, the wealth of spectral information provided by last-generation (hyperspectral) instruments has quickly introduced the need for parallel CBIR systems able to effectively retrieve features of interest from ever-growing data archives. To address this need, this paper develops a new parallel CBIR system which has been specifically designed to be run on heterogeneous networks of computers (HNOCs). These platforms have soon become a standard computing architecture in remote sensing missions due to the distributed nature of data repositories. The proposed heterogeneous system first extracts an image feature vector able to characterize image content with sub-pixel precision, and then uses the obtained feature as a search reference. The system is validated using a complex hyperspectral image database, and implemented on several networks of workstations at University of Maryland.
Assessing the Performance-Energy Balance of Graphics Processors for Spectral Unmixing

Remotely sensed hyperspectral imaging missions are often limited by onboard power restrictions while, simultaneously, require high computing power in order to address applications with relevant constraints in terms of processing times. In recent years, graphics processing units (GPUs) have emerged as a commodity computing platform suitable to meet real-time processing requirements in hyperspectral image processing. On the other hand, GPUs are power-hungry devices, which result in the need to explore the tradeoff between the expected high performance and the significant power consumption of computing architectures suitable to perform fast processing of hyperspectral images. In this paper, we explore the balance between computing performance and power consumption of GPUs in the context of a popular hyperspectral imaging application, such as spectral unmixing. Specifically, we investigate several processing chains for spectral unmixing and evaluate them on three different GPUs, corresponding to the two latest generations of GPUs from NVIDIA (“Fermi” and “Kepler”), as well as an alternative low-power system more suitable for embedded appliances. Our paper provides some observations about the possibility to use GPUs as effective onboard devices in hyperspectral imaging applications.
GPU Implementation of Spatial–Spectral Preprocessing for Hyperspectral Unmixing

Spectral unmixing pursues the identification of spectrally pure constituents, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in these spatial-spectral techniques, as hyperspectral images contain very rich information in both the spatial and spectral domains. In this letter, we develop a computationally efficient implementation of a spatial-spectral processing algorithm that has been successfully applied prior to the spectral unmixing of the hyperspectral data. Our implementation has been optimized for the commodity graphics processing units (GPUs) and is evaluated (using both synthetic and real data) using different GPU architectures. Significant speedups can be achieved when processing hyperspectral images of different sizes. This allows for the inclusion of the proposed parallel preprocessing module in a full hyperspectral unmixing chain able to operate in real time.
Stress free quad beam optical silicon accelerometer

An optical accelerometer is presented in this paper. The working principle is based on the variation of the output light intensity versus the acceleration. The variation is due to the misalignment of ARROW waveguides defined on the accelerometer mass and frame. The accelerometer consists of a quad beam structure with four lateral beams to increase the sensitivity. The technology for the fabrication of the mechanical structures is based on BESOI wafers combining bulk and surface micromachining. Numerical simulations by FEM have been done to optimize the devices. The devices have been tested dynamically and statically. The design, simulation, fabrication and preliminary results are presented.
Dose-free monitoring of radiotherapy treatments with scattered photons: First experimental results at a 6-MV Linac

Radiotherapy (RT) is nowadays, after surgery, among the most commonly used treatment regarding 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 multi-pixel imaging technique for real-time dose verification during photon RT has been proposed (RTmon). First scanned, single-pixel experimental results are presented here. The principle of operation of RTmon relies on the detection of scattered photons emitted perpendicularly to the beam direction. The first experimental 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, and (3) optimizing the collimation by means of detector positioning and differential signal sampling. Simulation and experimental data showed 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 shielding and differential signal sampling renders the scanned experimental results quite similar to those obtained by means of Geant4 simulations. Such experimental results suggest therefore that this detector system may be useful for real-time dose verification in external X-ray radiotherapy.
Impact of Initialization on Design of Endmember Extraction Algorithms

Many endmember extraction algorithms (EEAs) have been developed to find endmembers that are assumed to be pure signatures in hyperspectral data. However, two issues arising in EEAs have not been addressed: one is the knowledge of the number of endmembers that must be provided a priori, and the other is the initialization of EEAs, where most EEAs initialize their endmember-searching processes by using randomly generated endmembers, which generally result in inconsistent final selected endmembers. Unfortunately, there has been no previous work reported on how to address these two issues, i.e., how to select a set of appropriate initial endmembers and how to determine the number of endmembers p. This paper takes up these two issues and describes two-stage processes to improve EEAs. First, a recently developed concept of virtual dimensionality (VD) is used to determine how many endmembers are needed to be generated for an EEA. Experiments show that the VD is an adequate measure for estimating p. Second, since EEAs are sensitive to initial endmembers, a properly selected set of initial endmembers can make significant improvements on the searching process. In doing so, a new concept of endmember initialization algorithm (EIA) is thus proposed, and four different algorithms are suggested for this purpose. It is surprisingly found that many EIA-generated initial endmembers turn out to be the final desired endmembers. A further objective is to demonstrate that EEAs implemented in conjunction with EIA-generated initial endmembers can significantly reduce the number of endmember replacements as well as the computing time during endmember search
Oil Spill Detection via Multitemporal Optical Remote Sensing Images: A Change Detection Perspective

Oil spill monitoring in optical remote sensing (RS) images is a challenging task due to the complexity of target discrimination in an oil spill scenario. Differently from traditional oil spill detection methods that are mainly carried out in a monotemporal image, in this letter, a novel solution is given in a multitemporal domain by investigating potential capability of change detection (CD) techniques, and it mainly contributes to an unsupervised, semiautomatic, and efficient approach. It opens a new perspective for solving an oil spill detection problem. In particular, a coarse-to-fine multitemporal change analysis procedure is designed to investigate the spectral–temporal variation of change targets present in the scenario. Changes relevant and irrelevant to suspected oil spills are identified and discriminated according to a binary and a multiple CD process, respectively. The proposed approach provides a quick yet effective oil spill detection solution, which is valuable and important in practical applications. The proposed method was validated on two real multitemporal RS data sets presenting the oil spill event in northern Gulf of Mexico in 2010. Experimental results confirmed its effectiveness.
Dual-Mode FPGA Implementation of Target and Anomaly Detection Algorithms for Real-Time Hyperspectral Imaging

Target and anomaly detection are important techniques for remotely sensed hyperspectral data interpretation. Due to the high dimensionality of hyperspectral data and the large computational complexity associated to processing algorithms, developing fast techniques for target and anomaly detection has received considerable attention in recent years. Although several high-performance architectures have been evaluated for this purpose, field programmable gate arrays (FPGAs) offer the possibility of onboard hyperspectral data processing with low-power consumption, reconfigurability and radiation tolerance, which make FPGAs a relevant platform for hyperspectral processing. In this paper, we develop a novel FPGA-based technique for efficient target detection in hyperspectral images. The proposed method uses a streaming background statistics (SBS) approach for optimizing the constrained energy minimization (CEM) and Reed-Xiaoli (RX) algorithms, which are widely used techniques for target and anomaly detection, respectively. Specifically, these two algorithms are implemented in streaming fashion on FPGAs. Most importantly, we present a dual mode that implements a flexible datapath to decide in real time which one among these two algorithms should be used, thus allowing for the dynamic adaptation of the hardware to either target detection or anomaly detection scenarios. Our experiments, conducted with several well-known hyperspectral scenes, indicate the effectiveness of the proposed implementations.
A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery

Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have become very popular. However, such models are often based on a local averaging constraint, which does not allow for a detailed exploration of the spatial information, thus limiting their discriminative capability and preventing the spatial homogeneity of the clustering results. To address these relevant issues, in this letter, we develop a new and effective ℓ2-norm regularized SSC algorithm which adds a four-neighborhood ℓ2-norm regularizer into the classical SSC model, thus taking full advantage of the spatial-spectral information contained in HSIs. The experimental results confirm the potential of including the spatial information (through the newly added ℓ2-norm regularization term) in the SSC framework, which leads to a significant improvement in the clustering accuracy of SSC when applied to HSIs.
FEM of anodic bonding test structures

The presented work deals with the study of the electro-mechanical phenomena during the anodic bonding process between glass and silicon. Special test structures were designed for the characterisation of the bonding. The test structures consist of a matrix of circular and rectangular cavities defined by reactive ion etching on the silicon piece with different sizes and depths. During anodic bonding the structures deform. In the cavities with lower stiffness, the surfaces of the glass and silicon get into intimate contact and bond. The larger electrostatic pressure the more stiff structures will be bonded, therefore, the bonded structures give information about the electrostatic pressure. In this manuscript different electrostatic models are studied by using finite element modeling (ANSYS). The results are fitted with experimental data to validate the best model.
Informative Change Detection by Unmixing for Hyperspectral Images

Applying spectral unmixing on a series of multitemporal hyperspectral images for change detection has the potential to reveal important subpixel-level information, such as the abundance variation of each underlying material in a given location or the change in the distribution of materials throughout the scene, with time or resulting from significant events such as a natural disaster. However, change detection by spectral unmixing for hyperspectral images has not been extensively studied up to now, and most studies have been limited to specific cases and data sets. This is caused by the scarcity of real multitemporal hyperspectral data and the inherent difficulties in applying unmixing to multitemporal hyperspectral data in a coherent way. In this letter, we investigate change detection for hyperspectral images by spectral unmixing and systematically present the advantages that can be gained by using such an approach, supported by experimental studies conducted on carefully prepared synthetic data sets and also with real data sets.
12E-1 Accelerometer Based on Thin-Film Bulk Acoustic Wave Resonators

A MEMS accelerometer, its acceleration-sensing devices being FBARs, is described. The FBAR-based accelerometer is comprised by a movable, seismic mass, which is supported by one or more beams. Each beam embeds an FBAR or can be an FBAR by itself, whose resonant frequency changes when the beam bends due to the application of acceleration forces to the seismic mass. Due to mechanical coupling between the beams and the mass, the displacement of the latter produces a stress in the beams, which is transmitted to the FBAR low frequency and high frequency characterization of the resonant accelerometer has been carried out, a static-acceleration sensitivity of 250 kHz/g being found for the quad-beam, embedded-FBAR device.