nils-herrmann/clean_scirepeval huggingface dataset:
Spectral unmixing-based post-processing for hyperspectral image classification

Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, possible connections between classification and spectral unmixing concepts have been rarely investigated. In this work, we propose a simple spectral unmixing-based post-processing method to improve the classification accuracies provided by supervised and semi-supervised techniques for hyperspectral image classification. The proposed approach exploits the information retrieved with spectral unmixing in order to complement the results obtained after the classification stage (which can be either supervised or semi-supervised), thus bridging the gap between unmixing and classification and exploiting both techniques in synergistic fashion for hyperspectral data interpretation. The proposed method is experimentally validated using a real hyperspectral data set collected by the Reflective Optics Spectrographic Imaging System (ROSIS). Our experimental results indicate that the proposed unmixing-based preprocessing can improve the classification results for some of the classes, particularly the most highly mixed ones, in supervised and semi-supervised scenarios using limited training samples.
A comparative assessment of several processing chains for hyperspectral image classification: What features to use?

Hyperspectral image classification is a very active research area. Over the last years, several advanced feature extraction techniques have been integrated in processing chains intended for this purpose. In the context of supervised classification, the good generalization capability of machine learning techniques such as the support vector machine (SVM) can still be enhanced by an adequate selection of the number of features to be used for classification purposes. This number depends on the size of the available training set, which opens the way for the incorporation of supervised techniques for feature extraction in addition to more classic unsupervised ones. In this paper, we particularly investigate the issue of how many (and what type of) features can be used effectively for SVM-based classification. For this purpose, we consider different types of feature extraction strategies — unsupervised and supervised — in the context of different types of processing chains (all based on the SVM as the baseline classifier). We also explore the role of different dimensionality estimation techniques. Our study, conducted using a variety of hyperspectral scenes collected by different instruments, provides practical observations regarding the utility and number of features needed for different analysis scenarios.
Multiple Algorithm Integration Based on Ant Colony Optimization for Endmember Extraction From Hyperspectral Imagery

Spectral unmixing is an important technique in hyperspectral image exploitation. It comprises the extraction of a set of pure spectral signatures (called endmembers in hyperspectral jargon) and their corresponding fractional abundances in each pixel of the scene. Over the last few years, many approaches have been proposed to automatically extract endmembers, which is a critical step of the spectral unmixing chain. Recently, ant colony optimization (ACO) techniques have reformulated the endmember extraction issue as a combinatorial optimization problem. Due to the huge computation load involved, how to provide suitable candidate endmembers for ACO is particularly important, but this aspect has never been discussed before in the literature. In this paper, we illustrate the capacity of ACO techniques for integrating the results obtained by different endmember extraction algorithms. Our experimental results, conducted using several state-of-the-art endmember extraction approaches using both simulated and a real hyperspectral scene (cuprite), indicate that the proposed ACO-based strategy can provide endmembers which are robust against noise and outliers.
ARROW-based optical accelerometers

Design, fabrication and characterization of silicon-based stress-free self-aligned quad beam accelerometer is presented. Standard total internal reflection waveguides have been replaced by ARROW structures, enhancing the sensitivity to >4dB/g at the wavelength visible range (633nm) Moreover, as the waveguide core has approximately the same size as the input fiber optic, insertion losses have been reduced to 2.5dB. Attenuation in ARROW waveguides is 0.3dB/cm.
Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral–Spatial Features

Change detection (CD) is an active research topic in remote sensing applications including urban studies, disaster assessment, and deforestation monitoring. In this paper, we propose an automatic method for CD in high-resolution remote sensing images that uses a novel strategy for the selection of training samples and an ensemble of multiple classifiers. As for the selection of training samples, our proposed method uses two groups of thresholds instead of just one threshold to enhance the quality of the selected training samples by allowing for their selection in an intelligent manner. In order to achieve high CD accuracy, spatial information such as texture and morphological profiles are utilized in conjunction with spectral information. Our multiple classifier system (MCS) exploits the extreme learning machine (ELM), multinomial logistic regression (MLR), and K-nearest neighbor (KNN) classifiers. To validate our newly proposed approach, we conduct experiments using multispectral images collected by ZY-3. The proposed method provides state-of-the-art CD accuracies as compared with other approaches widely used in the literature for CD purposes.
Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation

Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifically, the proposed algorithm enhances the sparsity of fractional abundances in both spectral and spatial domains through the use of double weights, where one is used to enhance the sparsity of endmembers in spectral library, and the other is introduced to improve the sparsity of fractional abundances. Moreover, a TV-based regularization is further adopted to explore the spatial–contextual information. As such, the simultaneous utilization of both double reweighted  $l_{1}$  minimization and TV regularizer can significantly improve the sparse unmixing performance. Experimental results on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm both visually and quantitatively.
A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation

In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial–contextual information in the scene when conducting feature extraction (FE) from hyperspectral images (HSIs). Specifically, our method first projects the HSI into a subspace, using the maximum noise fraction method. Then, for each test pixel in the subspace, its most similar neighboring pixels (within a local spatial window) are clustered using the cosine distance measurement. The test pixel and its neighbors are used to calculate a local CM for FE purposes. Each nondiagonal entry in the matrix characterizes the correlation between different spectral bands. Finally, these matrices are used as spatial–spectral features and fed to a support vector machine for classification purposes. The proposed method offers a new strategy to characterize the spatial–spectral information in the HSI prior to classification. Experimental results have been conducted using three publicly available hyperspectral data sets for classification, indicating that the proposed method can outperform several state-of-the-art techniques, especially when the training samples available are limited.
Characterization of optical accelerometers based on UV-sensitive polymers

The design, simulation, fabrication, and characterization of quad-beam polymer optical accelerometers is presented in this paper. An applied acceleration causes a misalignment between the waveguides that comprise the structure. Two different geometries with different beam widths have been tested. Both of them show high mechanical and optical sensitivity and negligible cross sensitivities. Self-alignment structures for fast and accurate fiber-optic positioning have also been implemented in the device. Stable, simple, and robust technology that only requires two technological steps is also presented. Static characterization shows experimental optical sensitivities of at least 15.8 and 6.2 dB/g for each optical accelerometer.
Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification

Spectral–spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-rank Gabor filtering (DLRGF) method for spectral–spatial hyperspectral image classification. A main innovation of the proposed approach is that our implementation is accomplished by decomposing the standard 3-D spectral–spatial Gabor filter into eight subfilters, which correspond to different combinations of low-pass and bandpass single-rank filters. Then, we show that only one of the subfilters (i.e., the one that performs low-pass spatial filtering and bandpass spectral filtering) is actually appropriate to extract suitable features based on the characteristics of hyperspectral images. This allows us to perform spectral–spatial classification in a highly discriminative and computationally efficient way, by significantly decreasing the computational complexity (from cubic to linear order) compared with the 3-D spectral–spatial Gabor filter. In order to theoretically prove the discriminative ability of the selected subfilter, we derive an overall classification risk bound to evaluate the discriminating abilities of the features provided by the different subfilters. Our experimental results, conducted using different hyperspectral images, indicate that the proposed DLRGF method exhibits significant improvements in terms of classification accuracy and computational performance when compared with the 3-D spectral–spatial Gabor filter and other state-of-the-art spectral–spatial classification methods.
High performance computing for hyperspectral image analysis: Perspective and state-of-the-art

The main purpose of this paper is to describe available (HPC)-based implementations of remotely sensed hyperspectral image processing algorithms on multi-computer clusters, heterogeneous networks of computers, and specialized hardware architectures such as field programmable gate arrays (FPGAs) and graphic processing units (GPUs). Combined, the revision of existing techniques conducted in this paper, along with the description of performance results for a parallel hyperspectral processing chain on different architectures, delivers an excellent snapshot of the state-of-the-art in the area of HPC-based hyperspectral image processing and a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to hyperspectral imaging problems.
Sparse hyperspectral unmixing with spatial discontinuity preservation

Sparse unmixing and sparse representation are known to be effective for improving the interpretation of remotely sensed hyperspectral images. Classic methods for incorporating spatial information into spectral unmixing assume that the abundances of the pixels are smooth and fall into a homogeneous region shared by the same endmembers and their corresponding fractional abundances. Sometimes this assumption does not hold in practice, as images contain abrupt changes in endmember abundances, i.e., abundance maps are often spatially heterogeneous. To address this limitation, in this work we propose a novel sparse unmixing framework with discontinuity preservation which aims at preserving the spatial heterogeneity present in abundance maps. In the proposed framework, the Sobel operator is used to characterize such discontinuities. Our experimental results, conducted using both simulated and real hyperspectral data sets, indicate that the introduction of a discontinuity preserving strategy on sparse unmixing formulations is beneficial to preserve the spatial heterogeneity present in the abundance maps. We exploit this information effectively to improve abundance estimation.
Piezoresistive accelerometers for MCM-package - Part II:The packaging

This paper is the second part of a twofold work related to the full integration of piezoresistive accelerometers in a multichip modules-deposited (MCM-D) type with flip-chip interconnection package. This part is fully devoted to the packaging of an accelerometer with movable parts in an MCM so a description of the package fabrication is given. The accelerometer was designed taking into account its final package and, therefore, some modifications to the standard piezoresistive accelerometer were done. In particular, a sealing ring was defined around the movable parts of the sensor in order to protect them from the underfill used during the final packaging process. This underfill is being studied at the moment so the tests performed have been without taking into account its effects. After the manufacturing step the MCM packaged accelerometers are attached to a PLCC44 substrate and characterized. The results show an improvement on the sensors performance when using the MCM solution in comparison to the accelerometers packaged in standard TO-8. The results show that the influence of the packaging technique on the final stress of the sensors is negligible. They also show an extra damping due to the confined air volume around the swinging proof mass of the accelerometers. The quality factor of the TO-8 packaged accelerometers is of 12 whereas for the MCM packaged devices is of 1, 2. This low-quality factor is of great interest for applications where the devices can suffer large acceleration changes. Finally, the results are in good agreement with the FEM simulations previously done and they demonstrate the viability of this technology.
Joint linear/nonlinear spectral unmixing of hyperspectral image data

Many available techniques for spectral mixture analysis involve the separation of mixed pixel spectra collected by imaging spectrometers into pure component (endmember) spectra, and the estimation of abundance values for each end- member. Although linear mixing models generally provide a good abstraction of the mixing process, several naturally occurring situations exist where nonlinear models may provide the most accurate assessment of endmember abundance. In this paper, we propose a combined linear/nonlinear mixture model which makes use of linear mixture analysis to provide an initial model estimation, which is then thoroughly refined using a multi-layer neural network coupled with intelligent algorithms for automatic selection of training samples. Three different algorithms for automatic selection of training samples, such as border training algorithm (BTA), mixed signature algorithm (MSA) and mophological erosion algorithm (MEA) are developed for this purpose. The proposed model is evaluated in the context of a real application which involves the use of hyperspectral data sets, collected by the Digital Airborne (DAIS 7915) and Reflective Optics System (ROSIS) imaging spectrometers of DLR, operating simultaneously at multiple spatial resolutions.
Hyperspectral Unmixing on GPUs and Multi-Core Processors: A Comparison

One of the main problems in the analysis of remotely sensed hyperspectral data cubes is the presence of mixed pixels, which arise when the spatial resolution of the sensor is not able to separate spectrally distinct materials. Due to this reason, spectral unmixing has become one of the most important tasks for hyperspectral data exploitation. However, unmixing algorithms can be computationally very expensive, a fact that compromises their use in applications under real-time constraints. For this purpose, 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. The proposed full unmixing chain is composed for three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. The two computing platforms used in this work are inter-compared in the context of hyperspectral unmixing applications. The GPU implementation of the proposed methodology has been implemented using the compute devide unified architecture (CUDA) and the cuBLAS library, and tested on two different GPU architectures: NVidia™ GeForce GTX 580 and NVidia™ Tesla C1060. It provides real-time unmixing performance in two different analysis scenarios using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World Trade Center complex in New York City. The multi-core implementation, developed using the applications program interface (API) OpenMP and the Intel Math Kernel Library (MKL) used for matrix multiplications, achieved near real-time performance in the same scenarios. A comparison of both architectures in terms of performance, cost and mission payload considerations is given based on the results obtained in the two considered data analysis scenarios.
The Future of Imaging Spectroscopy Prospective Technologies and Applications

Spectroscopy has existed for more than three centuries now. Nonetheless, significant scientific advances have been achieved. We discuss the history of spectroscopy in relation to emerging technologies and applications. Advanced focal plane arrays, optical design, and intelligent on-board logic are prime prospective technologies. Scalable approaches in pre-processing of imaging spectrometer data will receive additional focus. Finally, we focus on new applications monitoring transitional ecological zones, where human impact and disturbance have highest impact as well as in monitoring changes in our natural resources and environment. We conclude that imaging spectroscopy enables mapping of biophysical and biochemical variables of the Earth's surface and atmospheric composition with unprecedented accuracy.
Efficient Multi-Band Texture Analysis for Remotely Sensed Data Interpretation in Urban Areas

Texture analysis is a long-standing and important problem in image-based urban characterization. A variety of approaches and methods have been proposed in the past to deal with urban texture segmentation and classification. However, texture characterization is particularly complex when the image data is composed of several spectral bands at different wavelengths, as in the case of remotely sensed hyperspectral images, in which hundreds of spectral bands are often available. Such images have two domains which can be analyzed: the spectral domain and the spatial domain. In this paper, we develop a joint spatial/spectral classification approach for hyperspectral imagery which is shown to perform effectively in highly complex urban environments. Experimental results are provided using a hyperspectral scene with extensive ground-truth, collected over the town of Pavia in Italy. To address the high computational requirements of the algorithm, we also develop a parallel implementation which is tested in this work using a massively parallel supercomputer at NASA's Goddard Space Flight Center in Maryland.
Characterisation of surface micromachined beams with floating gate transistor

A micromechanical beam structure has been fabricated with surface micromachining of a 2 /spl mu/m polysilicon layer and includes capacitive electrodes for excitation and floating gate MOS transistors for detection. The device is presented in two possible configuration that we call double gate (DG) or extended gate (EXTG). Preliminary results of the electrical characterisation and mechanical behaviour are presented.
Recent Developments in High Performance Computing for Remote Sensing: A Review

Remote sensing data have become very widespread in recent years, and the exploitation of this technology has gone from developments mainly conducted by government intelligence agencies to those carried out by general users and companies. There is a great deal more to remote sensing data than meets the eye, and extracting that information turns out to be a major computational challenge. For this purpose, high performance computing (HPC) infrastructure such as clusters, distributed networks or specialized hardware devices provide important architectural developments to accelerate the computations related with information extraction in remote sensing. In this paper, we review recent advances in HPC applied to remote sensing problems; in particular, the HPC-based paradigms included in this review comprise multiprocessor systems, large-scale and heterogeneous networks of computers, grid and cloud computing environments, and hardware systems such as field programmable gate arrays (FPGAs) and graphics processing units (GPUs). Combined, these parts deliver a snapshot of the state-of-the-art and most recent developments in those areas, and offer a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to remote sensing problems.
Parallel hyperspectral image processing on commodity graphics hardware

Many recent research efforts have been devoted to the use of commodity hardware for solving computationally-intensive scientific problems. Among such problems, hyperspectral imaging has created new processing challenges in the remote sensing community. Hyperspectral sensors are now capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. For instance, NASA is continuously gathering high-dimensional image data with hyperspectral sensors such as Jet Propulsion Laboratory's airborne visible-infrared imaging spectrometer (AVIRIS). The increasing programmability and parallelism of commodity graphics processing units (GPUs) makes them strong candidates for addressing some of these challenges. In this paper, we describe a GPU-based framework for implementation of hyperspectral image processing algorithms which takes advantage of multiple levels of parallelism found in modern GPUs. This framework is inexpensive, uses readily available PC graphics hardware boards, and provides the desired performance at the quality required. Experimental results are presented and discussed in the context of a realistic application, based on hyperspectral data collected by NASA's AVIRIS system