nils-herrmann/clean_scirepeval huggingface dataset:
Polymeric MOEMS Variable Optical Attenuator

In this letter, we take advantage of the high coefficient of thermal expansion (CTE) of a chemically amplified, epoxy-based negative polymer (SU-8) to define a low-power consumption polymeric variable optical attenuator that combines the working principles of microoptoelectromechanical systems and photonic lightwave circuits. The SU-8 symmetric structure comprises a seismic mass and four mechanical beams. Three multimode waveguides are defined on this structure: two of them are located on the frame and the third one in the middle of the seismic mass. Aluminum is used as a heater electrode extending over two of the mechanical beams and part of the seismic mass. When a dc voltage is applied, the mechanical beams bend, resulting in a misalignment between the waveguides. Experimental results have shown a power consumption of 12 mW at 20 dB with a working wavelength of 633nm emitted from a light-emitting diode
Parallel implementation of the simplex growing algorithm for hyperspectral unmixing using OpenCL

Many algorithms for spectral unmixing have been proposed in the last years applied on hyperspectral imaging. This process is composed by three stages where the extraction of endmembers is the most consuming step. However, endmember extraction algorithms (EEAs) can be computationally very expensive and its acceleration on parallel architectures is still an interesting and open problem. In this paper, we present a parallel implementation of the simplex growing algorithm for hyperspectral unmixing called P-SGA on different platforms using the OpenCL framework. The proposed implementation exploits the memory hierarchy to accelerate the parts of this method which are more time-consuming. The proposed algorithm is evaluated in terms of both accuracy and computational performance through Monte Carlo simulations using the following architectures: multi-core Xeon CPU, NVidia GeForce GTX 980 GPU and Intel Xeon Phi accelerator. Experiments are conducted using real hyperspectral data set revealing considerable acceleration factors, which satisfies the real-time constraints given by the data acquisition rate.
Spatial weighted sparse regression for hyperspectral image unmixing

Sparse unmixing of hyperspectral data is an important technique which aims at estimating the fractional abundances of endmembers (pure spectral components). It is well known that enforcing sparseness becomes a necessary process in sparse unmixing methods. To better exploit the sparsity in hyperspectral imagery, a double reweighted sparse unmixing algorithm has been proposed. However, it focusses on analyzing the hyperspectral data without fully incorporating the spatial information. To address this limitation, a spatial weighted sparse unmixing (SWSU) algorithm is proposed in this paper, which can take full advantage of the spatial information and further enhance the sparsity of the abundances. This is done by incorporating local neighborhood weights into the double reweighted sparse unmixing formulation. Experimental results on simulated hyperspectral data sets illustrate the good potential of the spatial weighted strategy for sparse unmixing introduced in this paper, which can greatly improve abundance estimation results.
Robust Matrix Discriminative Analysis for Feature Extraction From Hyperspectral Images

Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial–spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.
Minimum volume simplicial enclosure for spectral unmixing of remotely sensed hyperspectral data

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Linear spectral unmixing relies on two main steps: 1) identification of pure spectral constituents (endmembers), and 2) endmember abundance estimation in mixed pixels. One of the main problems concerning the identification of spectral endmembers is the lack of pure spectral signatures in real hyperspectral data due to spatial resolution and mixture phenomena happening at different scales. In this paper, we present a new method for endmember estimation which does not assume the presence of pure pixels in the input data. The method minimizes the volume of an enclosing simplex in the reduced space while estimating the fractional abundance of vertices in simultaneous fashion, as opposed to other volume-based approaches such as N-FINDR which inflate the simplex of maximumvolume that can be formed using available image pixels. Our experimental results and comparisons to other endmember extraction algorithms indicate promising performance of the method in the task of extracting endmembers from real hyperspectral data. In our experiments, we use laboratory-simulated forest scenes with known endmembers and fractional abundances due to their acquisition in a controlled environment using a real hyperspectral imaging instrument.
FPGA Design of an Automatic Target Generation Process for Hyperspectral Image Analysis

Onboard processing of remotely sensed hyper spectral data is a highly desirable goal in many applications. For this purpose, compact reconfigurable hardware modules such as field programmable gate arrays (FPGAs) are widely used. In this paper, we develop a new implementation of an automatic target generation process (ATGP) for hyper spectral images. Our implementation is based on a design methodology that starts from a high-level description in Matlab (or alternative C/C++) and obtains a register transfer level (RTL) description that can be ported to FPGAs. In order to validate our new implementation, we develop a quantitative and comparative study using two different FPGA architectures: Xilinx Virtex-5 and Altera Stratix-III Altera. Experimental results have been obtained in the context of a real application focused on the detection of mineral components over the Cup rite mining district (Nevada), using hyper spectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experimental results indicate that the proposed implementation can achieve peak frequency designs above 200MHz in the considered FPGAs, in addition to satisfactory results in terms of target detection accuracy and parallel performance. This represents a step forward towards the design of real-time onboard implementations of hyper spectral image analysis algorithms.
Characterization of Soil Erosion Indicators Using Hyperspectral Data From a Mediterranean Rainfed Cultivated Region

The determination of surface soil properties is an important application of remotely sensed hyperspectral imagery. Moreover, different soil properties can be associated with erosion processes, with significant implications for land management and agricultural uses. This study integrates hyperspectral data supported by morphological and physico-chemical ground data to identify and map soil properties that can be used to assess soil erosion and accumulation. These properties characterize different soil horizons that emerge at the surface as a consequence of the intensity of the erosion processes, or the result of accumulation conditions. This study includes: 1) field and laboratory characterization of the main soil types in the study area; 2) identification and definition of indicators of soil erosion and accumulation stages (SEAS); 3) compilation of the site-specific MEDiterranean Soil Erosion Stages (MEDSES) spectral library of soil surface characteristics using field spectroscopy; 4) using hyperspectral airborne data to determine a set of endmembers for different SEAS and introducing these into the support vector machine (SVM) classifier to obtain their spatial distribution; and 5) evaluation of the accuracy of the classification applying a field validation protocol. The study region is located within an agricultural region in Central Spain, representative of Mediterranean agricultural uses dominated by a gently sloping relief, and characterized by soils with contrasting horizons. Results show that the proposed method is successful in mapping different SEAS that indicate preservation, partial loss, or complete loss of fertile soils, as well as down-slope accumulation of different soil materials.
Fast spatial-spectral preprocessing for endmember extraction and spectral unmixing using graphic processing units

Linear spectral unmixing consists on the identification of spectrally pure constituents, called endmembers and their corresponding proportions or abundances using a linear model. Traditionally, most of the attention has been focussed on the exploitation of spectral information when identifying a set of endmembers and, only recently, some techniques try to take advantage of complementary information such as the one provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in most of these spatial-spectral based techniques, as hyperspectral images provide very rich information in both the spatial and the spectral domain. In this paper we provide a computationally efficient implementation of a spatial-spectral processing (SSPP) algorithm which can be used prior to endmember identification and spectral unmixing. Specifically we present an implementation optimized for commodity graphics processing units (GPUs), which is evaluated using two different GPU architectures from NVidia: GeForce GTX580 and GeForce GT740. Our experimental validation reveals that significant speedups can be achieved when processing hyperspectral images of different sizes.
The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends

Hyperspectral imaging is an important technique in remote sensing which is characterized by high spectral resolutions. With the advent of new hyperspectral remote sensing missions and their increased temporal resolutions, the availability and dimensionality of hyperspectral data is continuously increasing. This demands fast processing solutions that can be used to compress and/or interpret hyperspectral data onboard spacecraft imaging platforms in order to reduce downlink connection requirements and perform a more efficient exploitation of hyperspectral data sets in various applications. Over the last few years, reconfigurable hardware solutions such as field-programmable gate arrays (FPGAs) have been consolidated as the standard choice for onboard remote sensing processing due to their smaller size, weight, and power consumption when compared with other high-performance computing systems, as well as to the availability of more FPGAs with increased tolerance to ionizing radiation in space. Although there have been many literature sources on the use of FPGAs in remote sensing in general and in hyperspectral remote sensing in particular, there is no specific reference discussing the state-of-the-art and future trends of applying this flexible and dynamic technology to such missions. In this work, a necessary first step in this direction is taken by providing an extensive review and discussion of the (current and future) capabilities of reconfigurable hardware and FPGAs in the context of hyperspectral remote sensing missions. The review covers both technological aspects of FPGA hardware and implementation issues, providing two specific case studies in which FPGAs are successfully used to improve the compression and interpretation (through spectral unmixing concepts) of remotely sensed hyperspectral data. Based on the two considered case studies, we also highlight the major challenges to be addressed in the near future in this emerging and fast growing research area.
On the optimization of memory access to increase the performance of spatial preprocessing techniques on graphics processing units

The use of spatial information prior to spectral unmixing of hyperspectral data is a very active research line in recent years. There are many approximations that consider spatial characteristics of the data in order to guide the endmember identification/extraction procedure. 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 parts of the scene. The main concern regarding SPP and this kind of preprocessing techniques is that they are computational expensive, adding a significant burden to the spectral unmixing process which should be alleviated. In this paper we revisit and enhance a previously developed implementation of SPP for graphical processing units (GPUs) in order to increase its performance by exhaustively using the level one (L1)-cache level of the GPU. The performance of the proposed implementation is evaluated using an NVidiaTMGeForce GTX 580. Our experimental validation reveals that real-time processing performance can be obtained for real hyperspectral data sets collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).
Hyperspectral Unmixing Based on Local Collaborative Sparse Regression

Spectral unmixing is an important technique for hyperspectral data exploitation. In order to solve the unmixing problem using a collection of previously available spectral signatures (i.e., a spectral library), sparse unmixing aims at finding the optimal subset of endmembers to represent the pixels in a hyperspectral image. The classic collaborative unmixing globally assumes that all pixels in a hyperspectral scene share the same active set of endmembers. This assumption rarely holds in practice, as endmembers tend to appear localized in spatially homogeneous areas rather than spread over the whole image. To address this limitation, in this letter, we introduce a new strategy to preserve local collaborativity for sparse hyperspectral unmixing. The proposed approach, which is called local collaborative sparse unmixing, considers the fact that endmember signatures generally appear distributed in local spatial regions instead of uniformly distributed throughout the scene. The proposed approach, which includes spatial information in the standard collaborative formulation, has been experimentally validated using both simulated and real hyperspectral data sets.
A new method for target detection in hyperspectral imagery based on extended morphological profiles

Hyperspectral remote sensing increases the detectability of pixel-and subpixel-sized targets by exploiting the finer detail in the spectral signatures. In this paper, we describe a new unsupervised algorithm for the detection of both full pixel and mixed pixel targets in hyperspectral imagery. The proposed method automatically resolves targets by using extended mathematical morphology operations. The performance of the resulting detector is experimentally evaluated using simulated and real hyperspectral data collected by the NASA/Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer and the DLR Reflective Optics System Imaging Spectrometer (ROSIS).
Superpixel-Based Active Learning and Online Feature Importance Learning for Hyperspectral Image Analysis

The rapid development of multichannel optical imaging sensors has led to increased utilization of hyperspectral data for remote sensing. For classification of hyperspectral data, an informative training set is necessary for ensuring robust performance. However, in remote sensing and other image analysis applications, labeled samples are often difficult, expensive, and time-consuming to obtain. This makes active learning (AL) an important part of an image analysis framework-AL aims to efficiently build a representative and efficient library of training samples that are most informative for the underlying classification task. This paper proposes an AL framework that leverages from superpixels. A spatial-spectral AL method is proposed that integrates spatial and spectral features extracted from superpixels in an AL framework. The experiments with an urban land cover classification and a wetland vegetation mapping task show that the proposed method has faster convergence and superior performance as compared to state of the art approaches. Additionally, our proposed framework has a key additional benefit in that it is able to identify and quantify feature importance - the resulting insights can be highly valuable to various remote sensing image analysis tasks.
FPGA Implementation of an Algorithm for Automatically Detecting Targets in Remotely Sensed Hyperspectral Images

Timely detection of targets continues to be a relevant challenge for hyperspectral remote sensing capability. The automatic target-generation process using an orthogonal projection operator (ATGP-OSP) has been widely used for this purpose. Hyperspectral target-detection applications require timely responses for swift decisions, which depend upon (near) real-time performance of algorithm analysis. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present an FPGA implementation for the ATGP-OSP algorithm. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real 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 in New York. Experimental results demonstrate that our hardware version of the ATGP-OSP algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.
Fusion of Hyperspectral and LiDAR Remote Sensing Data Using Multiple Feature Learning

Hyperspectral image classification has been an active topic of research. In recent years, it has been found that light detection and ranging (LiDAR) data provide a source of complementary information that can greatly assist in the classification of hyperspectral data, in particular when it is difficult to separate complex classes. This is because, in addition to the spatial and the spectral information provided by hyperspectral data, LiDAR can provide very valuable information about the height of the surveyed area that can help with the discrimination of classes and their separability. In the past, several efforts have been investigated for fusion of hyperspectral and LiDAR data, with some efforts driven by the morphological information that can be derived from both data sources. However, a main challenge for the learning approaches is how to exploit the information coming from multiple features. Specifically, it has been found that simple concatenation or stacking of features such as morphological attribute profiles (APs) may contain redundant information. In addition, a significant increase in the number of features may lead to very high-dimensional input features. This is in contrast with the limited number of training samples often available in remote-sensing applications, which may lead to the Hughes effect. In this work, we develop a new efficient strategy for fusion and classification of hyperspectral and LiDAR data. Our approach has been designed to integrate multiple types of features extracted from these data. An important characteristic of the presented approach is that it does not require any regularization parameters, so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a hyperspectral image and a LiDAR-derived digital surface model (DSM) collected over the University of Houston campus and the neighboring urban area, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results.
Heterogeneous Parallel Computing in Remote Sensing Applications: Current Trends and Future Perspectives

Heterogeneous networks of computers have rapidly become a very promising commodity computing solution, expected to play a major role in the design of high performance computing systems for remote sensing missions. Currently, only a few parallel processing strategies are available in this research area, and most of them assume homogeneity in the underlying computing platform. This paper develops several highly innovative heterogeneous parallel algorithms for information extraction from high-dimensional remotely sensed images, with particular emphasis on target detection and land-cover mapping applications. Experimental results are presented in the context of a realistic application, using real data collected by NASA's Jet Propulsion Laboratory over the World Trade Center complex in New York City after September 11th, 2001. Parallel performance of the proposed algorithms is discussed using several (fully and partially) heterogeneous networks at University of Maryland, and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Combined, these parts deliver a snapshot of the state-of-the-art in those areas, and a thoughtful perspective on the potential and challenges of applying heterogeneous computing practices to remote sensing problems
Segmentation as postprocessing for hyperspectral image classification

Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images at differents wavelength values for the same area of the Earth. For instance the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor of NASA capable to obtain 224 spectral channels in a wavelength range between 40 and 250 nanometers. As a result each pixel of the image can be represented as a spectral signature. Image segmentation is the process of dividing a digital image into groups of pixels or objects. Hyperspectral image classification is an important and active area dedicated to identifying each pixel in the image with an exclusive material/object class. Several efforts had been done in this field using spectral and spatial information separately or simultaneously in order to improve the performance of the classification techniques. In this work we have developed a new technique that uses a segmentation algorithm to post-process the classification results obtained using a widely used classifier such as the support vector machine (SVM). Experimental results with a real hyperspectral data set collected over the city of Pavia, Italy, are provided.