nils-herrmann/clean_scirepeval huggingface dataset:
Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

In recent years, the support vector machines (SVMs) have been very successful in remote sensing image classification, particularly when dealing with high-dimensional data and limited training samples. Nevertheless, the vector-based feature alignment of the SVM can lead to an information loss in representation of hyperspectral images, which intrinsically have a tensor-based data structure. In this paper, a new multiclass support tensor machine (STM) is specifically developed for hyperspectral image classification. Our newly proposed STM processes the hyperspectral image as a data cube and then identifies the information classes in tensor space. The multiclass STM is developed from a set of binary STM classifiers using the one-against-one parallel strategy. As a part of our tensor-based processing chain, a multilinear principal component analysis (MPCA) is used for preprocessing, in order to reduce the tensorial data redundancy and, at the same time, preserve the tensorial structure information in sparse and high-order subspaces. As a result, the contributions of this work are twofold: a new multiclass STM model for hyperspectral image classification is developed, and a tensorial image interpretation framework is constructed, which provides a system consisting of tensor-based feature representation, feature extraction, and classification. Experiments with four hyperspectral data sets, covering agricultural and urban areas, are conducted to validate the effectiveness of the proposed framework. Our experimental results show that the proposed STM and MPCA-STM can achieve better results than traditional SVM-based classifiers.
On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS, HyMap, and Hyperion with very high spectral resolution (i.e., high number of spectral channels) containing rich information for a wide range of applications. A relevant example is the separation of different types of land-cover classes using the data in order to understand, e.g., impacts of natural disasters or changing of city buildings over time. More recently, such increases in the data volume, velocity, and variety of data contributed to the term big data that stand for challenges shared with many other scientific disciplines. On one hand, the amount of available data is increasing in a way that raises the demand for automatic data analysis elements since many of the available data collections are massively underutilized lacking experts for manual investigation. On the other hand, proven statistical methods (e.g., dimensionality reduction) driven by manual approaches have a significant impact in reducing the amount of big data toward smaller smart data contributing to the more recently used terms data value and veracity (i.e., less noise, lower dimensions that capture the most important information). This paper aims to take stock of which proven statistical data mining methods in remote sensing are used to contribute to smart data analysis processes in the light of possible automation as well as scalable and parallel processing techniques. We focus on parallel support vector machines (SVMs) as one of the best out-of-the-box classification methods.
An iterative enhancement of higher order nonlinear mixture model for accurate hyperspectral unmixing

In order to provide a careful description of the interactions among endmembers in hyperspectral images, a new method for adaptive design of mixture models for hyperspectral unmixing is introduced. Specifically, the proposed approach relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers' spectra. Then, an iterative process is deployed in order to understand the order of local nonlinearity that is displayed by each endmember over every pixel. Experimental results show that the proposed approach is actually able to retrieve thorough information on the nature of the nonlinear effects over the image while providing excellent performance in reconstructing the given dataset.
Spatial-spectral preprocessing for volume-based endmember extraction algorithms using unsupervised clustering

Spectral unmixing is an important task in hyperspectral data exploitation. This approach first identifies a collection of spectrally pure constituent spectra, called endmembers, and then expresses the measured spectrum of each mixed pixel as a combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember present in the pixel. Over the last decade, several algorithms have been developed for automatic extraction of spectral end-members using volume-based concepts. These algorithms use the spectral information contained in the data, and often neglect the spatial information. In this paper, we develop a novel spatial-spectral preprocessing technique for volume-based endmember extraction algorithms intended to exploit spectral information more effectively by adequately incorporating spatial context. Our experimental results, conducted using a real hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite Mining district in Nevada, reveal that the proposed approach can successfully integrate the spatial and spectral information contained in the input hyperspectral data.
A New Cloud Computing Architecture for the Classification of Remote Sensing Data

This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applications by researchers from different scientific investigation fields. The implementation of architecture is also discussed. A software prototype (available online), which runs machine learning routines implemented on the cloud using the Waikato Environment for Knowledge Analysis (WEKA), a popular free software licensed under the GNU General Public License, is used for validation. Performance issues are addressed through an experimental analysis in which two supervised classifiers available in WEKA were used: random forest and support vector machines. This paper further describes how to include other classification methods in the available software prototype
GPU Parallel Implementation of Spatially Adaptive Hyperspectral Image Classification

Image classification is a very important tool for remotely sensed hyperspectral image processing. Techniques able to exploit the rich spectral information contained in the data, as well as its spatial-contextual information, have shown success in recent years. Due to the high dimensionality of hyperspectral data, spectral-spatial classification techniques are quite demanding from a computational viewpoint. In this paper, we present a computationally efficient parallel implementation for a spectral-spatial classification method based on spatially adaptive Markov random fields (MRFs). The method learns the spectral information from a sparse multinomial logistic regression classifier, and the spatial information is characterized by modeling the potential function associated with a weighted 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. It optimizes the work allocation and input/output transfers between the central processing unit and the GPU, taking full advantages of the computational power of GPUs as well as the high bandwidth and low latency of shared memory. As a result, the algorithm exploits the massively parallel nature of GPUs to achieve significant acceleration factors (higher than 70x) with regards to the serial and multicore versions of the same classifier on an NVIDIA Tesla K20C platform.
Hyperspectral and lidar data integration and classification

Light Detection and Ranging (LiDAR) is a technology used in different topic (mapping, urban land cover, agriculture, forestry, etc.). The great potential of LiDAR data lies in its high accuracy in the measurement of heights. Hyperspectral images, which comprise hundreds of (nearly contiguous) spectral channels, can also have spatial resolution of up to 1-5 meters per pixel. In this work, we propose to integrate both hyperspectral and LiDAR data by adding the LiDAR information to the hyperspectral data cube and correcting the geometric distortions. After arranging both data sets in the same format, we analyzed the errors obtained for each data source in order to determine if the final resolution adopted was the most appropriate one for performing data fusion. Our experimental results, in an area of Extremadura, indicate improvements in the classification after integrating the hyperspectral and LiDAR data.
Piezoresistive accelerometers for MCM package

Describes the first steps carried out for the integration of piezoresistive accelerometers in an MCM-D (D-type multichip modules with flip-chip interconnection) package. The bulk micromachined accelerometer technology and its modification to comply with MCM-D packaging technology requirements are presented. The accelerometer technology is based on BESOI (Bond and Etch Back Silicon-On-Insulator) wafers. The main characteristic of this technology is the use of the buried silicon oxide layer as an etch stop and as a sacrificial layer. In addition, over-range protection and self-test systems are defined without any additional photolithographic step or process. The flip chip attachment requires solderable metals in the bump pads. In addition, a sealing ring has been defined around the movable parts of the sensors to protect them from the underfill used during the final packaging process. Cantilever beam accelerometers with a self-test system are presented as example of the combined technology. The design, simulation, fabrication and characterization of the devices prior to the MCM-D packaging are presented as well.
Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing

Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to estimating the abundance of pure spectral constituents (endmembers) in each (possibly mixed) observation collected by the imaging instrument. In recent years, several endmember extraction algorithms (EEAs) have been proposed for automated endmember extraction from hyperspectral data sets. Traditionally, EEAs extract/select only one single standard endmember spectrum for each of the presented endmember classes or scene components. The use of fixed endmember spectra, however, is a simplification since in many cases the conditions of the scene components are spatially and temporally variable. As a result, variation in endmember spectral signatures is not always accounted for and, hence, spectral unmixing can lead to poor accuracy of the estimated endmember fractions. Here, we address this issue by developing a simple strategy to adapt available EEAs to select multiple endmembers (or bundles) per scene component. We run the EEAs in randomly selected subsets of the original hyperspectral image, and group the extracted samples of pure materials in a bundle using a clustering technique. The output is a spectral library of pure materials, extracted automatically from the input scene. The proposed technique is applied to several common EEAs and combined with an endmember variability reduction technique for unmixing purposes. Experiments with both simulated and real hyperspectral data sets indicate that the proposed strategy can significantly improve fractional abundance estimations by accounting for endmember variability in the original hyperspectral data.
Hyperspectral change detection by sparse unmixing with dictionary pruning

Hyperspectral change detection is used in many applications ranging from environmental monitoring to city planning and military surveillance. Change detection by unmixing has the potential of not only providing the locations of the changes, but also the nature of the change, and sub-pixel level information. Change detection by sparse unmixing using spectral libraries, with respect to regular spectral unmixing, has the added benefits of circumventing the process of endmember extraction and providing specific mission-based information. However, sparse unmixing is generally a severely ill-conditioned and time-consuming problem. Recently, an approach that aims to identify a subset of signatures of the spectral library that contribute most to the observed data has been proposed in order to improve the conditioning of the problem, hence decreasing computation time and enhancing performance. In this work, sparse unmixing with dictionary pruning is explored for change detection in multi-temporal hyperspectral images. Experimental results validate the performance of the proposed approach.
Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation

Hyperspectral unmixing is an important processing step for many hyperspectral applications, mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation of the abundance of each endmember in each pixel of the image. In recent years, nonnegative matrix factorization (NMF) has been highly attractive for this purpose due to the nonnegativity constraint that is often imposed in the abundance estimation step. However, most of the existing NMF-based methods only consider the information in a single layer while neglecting the hierarchical features with hidden information. To alleviate such limitation, in this paper, we propose a new sparsity-constrained deep NMF with total variation (SDNMF-TV) technique for hyperspectral unmixing. First, by adopting the concept of deep learning, the NMF algorithm is extended to deep NMF model. The proposed model consists of pretraining stage and fine-tuning stage, where the former pretrains all factors layer by layer and the latter is used to reduce the total reconstruction error. Second, in order to exploit adequately the spectral and spatial information included in the original hyperspectral image, we enforce two constraints on the abundance matrix. Specifically, the  $L_{1/2}$  constraint is adopted, since the distribution of each endmember is sparse in the 2-D space. The TV regularizer is further introduced to promote piecewise smoothness in abundance maps. For the optimization of the proposed model, multiplicative update rules are derived using the gradient descent method. The effectiveness and superiority of the SDNMF-TV algorithm are demonstrated by comparing with other unmixing methods on both synthetic and real data sets.
Nanomagnet fabrication on FBAR for magnetic sensor applications

We report a focused-ion-beam-assisted technique intended for ultra-small, hard magnet fabrication and placement on thin-film bulk acoustic resonators (FBAR). Reduced-size NdFeB magnets were extracted from a macroscopic quarry and bonded to the surface of FBARs by ion-beam-induced milling and deposition. Experimental measurements confirm the success of the method. S-parameter extraction was performed before and after nanomagnet attachment. Preliminary finite-element analysis has predicted force sensitivity at fundamental frequencies (kHz-MHz).
Real-Time Implementation of the Sparse Multinomial Logistic Regression for Hyperspectral Image Classification on GPUs

In this letter, a real-time implementation of the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm for sparse multinomial logistic regression is presented on commodity graphics processing units (GPUs) using Nvidia's compute unified device architecture. The proposed parallel method properly exploits the GPU architecture at the low level, including its shared memory, and takes full advantage of the computational power of GPUs to achieve real-time classification performance of hyperspectral images for the first time in the hyperspectral imaging literature. Our experimental results reveal remarkable acceleration factors and real-time performance, while retaining exactly the same classification accuracy with regard to the serial and multicore versions of the classifier.
Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data

Spectral mixture analysis is an important technique to analyze remotely sensed hyperspectral data sets. This approach involves the separation of a mixed pixel into its pure components or endmember spectra, and the estimation of the abundance value for each endmember. Several techniques have been developed for extraction of spectral endmembers and estimation of fractional abundances. However, an important issue that has not been yet fully accomplished is the incorporation of spatial constraints into endmember extraction and, particularly, fractional abundance estimation. Another relevant topic is the use of nonlinear versus linear mixture models, which can be unconstrained or constrained in nature. Here, the constraints refer to non-negativity and sum to unity of estimated fractional abundances for each pixel vector. In this paper, we investigate the impact of including spatial and abundance-related constraints in spectral mixture analysis of remotely sensed hyperspectral data sets. For this purpose, we discuss the advantages that can be obtained after including spatial information in techniques for endmember extraction and fractional abundance estimation, using a database of synthetic hyperspectral scenes with artificial spatial patterns generated using fractals, and a real hyperspectral scene collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).
Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors

This paper discusses the application and adaptation of two existing operational algorithms for land surface emissivity (epsiv) retrieval from different operational satellite/airborne sensors with bands in the visible and near-infrared (VNIR) and thermal IR (TIR) regions: (1) the temperature and emissivity separation algorithm, which retrieves epsiv only from TIR data and (2) the normalized-difference vegetation index thresholds method, in which epsiv is retrieved from VNIR data.
Sparse Unmixing-Based Content Retrieval of Hyperspectral Images on Graphics Processing Units

Content-based image retrieval (CBIR) systems have gained significant importance in the remotely sensed hyperspectral imaging community due to the increasing availability of hyperspectral data collected from different instruments. Spectral unmixing has been a popular technique for not only interpreting hyperspectral images but also retrieving them precisely from databases based on information content. This is due to the fact that the information provided by unmixing (i.e., the spectrally pure components of the scene or endmembers, and their corresponding abundance fractions) provides a very intuitive way to describe the content of the scene in both the spectral and the spatial sense. In this letter, we present a new computationally efficient CBIR system for hyperspectral data (available online: http://hypercomp. es/repositorySparse) which uses sparse unmixing concepts to retrieve hyperspectral scenes, based on their content, from large repositories. The search is guided by a spectral library, which is used as a guide to retrieve the data in a robust and efficient way. Given the large size of libraries and the sparsity of the unmixing solutions, the incorporation of sparse unmixing to the CBIR engine brings significant advantages. To optimize its performance in computational terms, the system has been implemented in parallel by taking advantage of the computational power of commodity graphics processing units. The proposed system is validated using a collection of synthetic and real hyperspectral images, exhibiting state-of-the-art performance.
Spatial/Spectral analysis of hyperspectral image data

The integration of spatial and spectral responses in hyperspectral image data analysis has been identified as a desirable objective by the remote sensing community. However, most available attempts are based on the consideration of spectral information separately from spatial information, and thus the two types of information are not treated simultaneously. In this paper, we describe our background in applying joint spatial/spectral techniques for full (pure)- and mixed-pixel classification of hyperspectral image data. Most of the techniques described in this work are based on classic mathematical morphology theory, which provides a remarkable framework to achieve the desired integration. The performance of the proposed methodologies is demonstrated by comparing them to other well-known pure- and mixed-pixel classifiers, using both simulated and real hyperspectral data collected by the NASA/JPL-AVIRIS and DLR-DAIS 7915 imaging spectrometers.
Spectral characterisation of land surface composition to determine soil erosion within semiarid rainfed cultivated areas

In a Mediterranean semiarid area in Central Spain, with dominant rainfed agriculture, hyperspectral airborne data, supported by field spectroscopy have been obtained, allowing a spectral identification of bare soil with the corresponding erosion stages. The definition of soil erosion stages is based on a spectral characterization supported by morphological, physical and chemical features of contrasted soil surfaces as a result of soil loss. Different soil erosion stages were defined within two bare-soil sites representing the soil variability of the area, and where such stages are spatially represented. The validation of selected image derived endmebers was a key step to carry out a partial unmixing for determining soil erosion stages. A preliminary spatial distribution of advanced and intermediate erosion stages was obtained for the most representative soil types.
Approximate Computing of Remotely Sensed Data: SVM Hyperspectral Image Classification as a Case Study

Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed onboard processing tasks. As a case study, the implementation of support vector machine (SVM) hyperspectral image classification is considered by using the proposed approximate computing framework. Experimental results show that the proposed approximate computing scheme achieves up to 70% power savings in the kernel accumulation computation procedure with negligible degradation of classification accuracy as compared to the traditional ripple carry adder (RCA) precise computation. This is an important achievement to meet the restrictions of onboard processing scenarios.