nils-herrmann/clean_scirepeval huggingface dataset:
GPU implementation of the pixel purity index algorithm for hyperspectral image analysis

Hyperspectral imaging is a new technique in remote sensing that generates images with hundreds of spectral bands, at different wavelength channels, for the same area on the surface of the Earth. The price paid for such a wealth of spectral information is the enormous amounts of data to be processed. In recent years, several efforts have been directed towards the incorporation of high-performance computing models in remote sensing missions. For this purpose, graphics processing units (GPUs) have emerged as a very interesting type of hardware architecture in hyperspectral image processing due to its low weight and compact size, which allows for on-board data processing. In this paper, we develop an innovative GPU implementation of a standard hyperspectral image processing algorithm called pixel purity index (PPI) and utilized, among others, in commercial software tools such as ITTVIS Environment for Visualizing Images (ENVI) software originally developed by Analytical Imaging and Geophysics (AIG), one of the most popular tools currently available for processing remotely sensed data. The algorithm has been implemented using the compute device unified architecture (CUDA), and tested on the NVidia Tesla C1060 architecture, achieving a significant performance increase in the analysis of both synthetic and real hyperspectral data.
Improved signal unmixing of vegetation using sparse group selection

Recently, signal unmixing was proposed in remote sensing with the goal to infer physical parameters of materials of interest, such as vegetation, on the ground. The typical approach uses large collections of pure spectra, called spectral libraries, in which many possible states of the vegetation are modeled by simulated or on-site acquired spectra. Spectra randomly selected from these libraries are used as input to dedicated unmixing methods, such as Multiple Endmember Spectral Mixture Analysis (MESMA). The spectra leading to the lowest reconstruction error are considered to be representative for the materials present in the pixel, such that the physical parameters of the ground vegetation can be inferred. However, the large number of spectra in the library imposes limits to the performance of this combinatorial approach, mainly related to running time constraints. In this paper, we propose the inclusion of a pre-processing step in the processing chain, based on the group lasso, which has the goal of selecting groups of signatures likely to be present in the mixtures. In this sense, the Group Sparse Unmixing via variable Splitting and Augmented Lagrangian (GSUnSAL) algorithm is used. The signatures contained in the groups selected by GSUnSAL are then used as input for MESMA. Our experiments using a real dataset acquired by an ASD spectrometer in a South-African orchard show that the proposed approach introduces important improvements in the signal unmixing solutions.
HyperMix: A new tool for quantitative evaluation of end member identification and spectral unmixing techniques

In this paper, we present a new open source system for evaluating and inter-comparing new spectral unmixing applications. The proposed tool, called HyperMix, comprises several open source implementations of algorithms for end member identification and spectral unmixing. The tool also includes a database of synthetic hyperspectral images (generated using fractals to simulate natural patterns) which can be used to evaluate the precision of the algorithms for end member identification and abundance estimation which are already incorporated in the tool. The paper also presents an exhaustive inter-comparison of algorithms for end member extraction and abundance estimation using the proposed tool.
FPGA Implementation of Abundance Estimation for Spectral Unmixing of Hyperspectral Data Using the Image Space Reconstruction Algorithm

One of the most popular and widely used techniques for analyzing remotely sensed hyperspectral data is spectral unmixing, which relies on two stages: (i) identification of pure spectral signatures (endmembers) in the data, and (ii) estimation of the abundance of each endmember in each (possibly mixed) pixel. Due to the high dimensionality of the hyperspectral data, spectral unmixing is a very time-consuming task. With recent advances in reconfigurable computing, especially using field programmable gate arrays (FPGAs), hyperspectral image processing algorithms can now be accelerated for on-board exploitation using compact hardware components with small size and cost. Although in previous work several efforts have been directed towards FPGA implementation of endmember extraction algorithms, the abundance estimation step has received comparatively much less attention. In this work, we develop a parallel FPGA-based design of the image space reconstruction algorithm (ISRA), a technique for solving linear inverse problems with positive constraints that has been used to estimate the abundance of each endmember in each pixel of a hyperspectral image. It is an iterative algorithm that guarantees convergence (after a certain number of iterations) and positive values in the results of the abundances (an important consideration in unmixing applications). Our system includes a direct memory access (DMA) module and implements a pre-fetching technique to hide the latency of the input/output communications. The method has been implemented on a Virtex-4 XC4VFX60 FPGA (a model that is similar to radiation-hardened FPGAs certified for space operation) and tested using real hyperspectral data sets collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the Jasper Ridge Biological Preserve in California. Experimental results demonstrate that our hardware version can significantly outperform an equivalent software version, thus being able to provide abundance estimation results in near real-time, which makes our reconfigurable system appealing for on-board hyperspectral data processing.
HyperMix: An Open-Source Tool for Fast Spectral Unmixing on Graphics Processing Units

Spectral unmixing has been a popular technique for analyzing remotely sensed hyperspectral images. The goal of unmixing is to find a collection of pure spectral constituents (called endmembers) that can explain each (possibly mixed) pixel of the scene as a combination of endmembers, weighted by their coverage fractions in the pixel or abundances. Over the last years, many algorithms have been presented to address the three main parts of the spectral unmixing chain: 1) estimation of the number of endmembers; 2) identification of the endmember signatures; and 3) estimation of the per-pixel fractional abundances. However, to date, there is no standardized tool that integrates these algorithms in a unified framework. In this letter, we present HyperMix, an open-source tool for spectral unmixing that integrates different approaches for spectral unmixing and allows building unmixing chains in graphical fashion, so that the end-user can define one or several spectral unmixing chains in fully configurable mode. HyperMix provides efficient implementations of most of the algorithms used for spectral unmixing, so that the tool automatically recognizes if the computer has a graphics processing unit (GPU) available and optimizes the execution of these algorithms in the GPU. This allows for the execution of spectral unmixing chains on large hyperspectral scenes in computationally efficient fashion. The tool is available online from http://hypercomphypermix. blogspot.com.es and has been validated with real hyperspectral scenes, providing state-of-the-art unmixing results.
Cloud Implementation of a Full Hyperspectral Unmixing Chain Within the NASA Web Coverage Processing Service for EO-1

The launch of the NASA Earth Observing 1 (EO-1) platform in November 2000 marked the establishment of spaceborne hyperspectral technology for land imaging. The Hyperion sensor onboard EO-1 operates in the 0.4-2.5 micrometer spectral range, with 10 nanometer spectral resolution and 30-meter spatial resolution. Spectral unmixing has been one of the most successful approaches to analyze Hyperion data since its launch. It estimates the abundance of spectrally pure constituents (endmembers) in each observation collected by the sensor. Due to the high spectral dimensionality of Hyperion data, unmixing is a very time-consuming operation. In this paper, we develop a cloud implementation of a full hyperspectral unmixing chain made up of the following steps: 1) dimensionality reduction; 2) automatic endmember identification; and 3) fully constrained abundance estimation. The unmixing chain will be available online within the Web Coverage Processing Service (WCPS), an image processing framework that can run on the cloud, as part of the NASA SensorWeb suite of web services. The proposed implementation has been demonstrated using the EO-1 Hyperion imagery. Our experimental results with a hyperspectral scene collected over the Okavango Basin in Botswana suggest the (present and future) potential of spectral unmixing for improved exploitation of spaceborne hyperspectral data. The integration of the unmixing chain in the WCPS framework as part of the NASA SensorWeb suite of web services is just the start of an international collaboration in which many more processing algorithms will be made available to the community through this service. This paper is not so much focused on the theory and results of unmixing (widely demonstrated in other contributions) but about the process and added value of the proposed contribution for ground processing on the cloud and onboard migration of those algorithms to support the generation of low-latency products for new airborne/spaceborne missions.
Thin Cloud Removal Based on Signal Transmission Principles and Spectral Mixture Analysis

Cloud removal is an important goal for enhancing the utilization of optical remote sensing satellite images. Clouds dynamically affect the signal transmission due to their different shapes, heights, and distribution. In the case of thick opaque clouds, pixel replacement has been commonly adopted. For thin clouds, pixel correction techniques allow the effects of thin clouds to be removed while retaining the remaining information in the contaminated pixels. In this paper, we develop a new method based on signal transmission and spectral mixture analysis for pixel correction which makes use of a cloud removal model that considers not only the additive reflectance from the clouds but also the energy absorption when solar radiation passes through them. Data correction is achieved by subtracting the product of the cloud endmember signature and the cloud abundance and rescaling according to the cloud thickness. The proposed method has no requirement for meteorological data and does not rely on reference images. Our experimental results indicate that the proposed approach is able to perform effective removal of thin clouds in different scenarios.
Characterization and passivation effects of an optical accelerometer based on antiresonant waveguides

An optical accelerometer based on antiresonant reflecting optical waveguides is presented. The design consists on a quad beam structure where two waveguides are placed on the frame of the accelerometer, while a third is placed over the proof mass. When an acceleration is applied, a misalignment between the three waveguides is immediate, causing a reduction in the output power. If the passivation layer is not considered, simulations predict a nearly symmetrical behavior for positive and negative accelerations, with an optical sensitivity of 2.3 dB/g. Experimental and simulation confirm that passivation modes produce a reduction in the optical sensitivity for positive acceleration values, resulting in an asymmetrical response of the accelerometer.
Spectrometer-Driven Spectral Partitioning for Hyperspectral Image Classification

Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for hyperspectral image classification assume that the spectral signatures provided by an imaging spectrometer can be interpreted as a unique and continuous signal, in practice, this signal may be obtained after the combination of several individual responses obtained from different spectrometers. In this work, we propose a new spectral partitioning strategy prior to classification which takes into account the physical design of the imaging spectrometer system for partitioning the spectral bands collected by each spectrometer, and resampling them into different groups or partitions. The final classification result is obtained as a combination of the results obtained from each individual partition by means of a multiple classifier system (MCS). The proposed strategy not only incorporates the design of the imaging spectrometer into the classification process but also circumvents problems such as the curse of dimensionality given by the unbalance between the high number of spectral bands and the generally limited number of training samples available for classification purposes. This concept is illustrated in this work using two different imaging spectrometers: the airborne visible infra-red imaging spectrometer, operated by NASA, and the digital airborne imaging system (DAIS), operated by the German Aerospace Center. Our experiments indicate that the proposed spectral partitioning strategy can lead to classification improvements on the order of 5% overall accuracy when using state-of-the-art spatial-spectral classifiers with very limited training samples.
Parallel Morphological Endmember Extraction Using Commodity Graphics Hardware

Spatial/spectral algorithms have been shown in previous work to be a promising approach to the problem of extracting image end members from remotely sensed hyperspectral data. Such algorithms map nicely on high-performance systems such as massively parallel clusters and networks of computers. Unfortunately, these systems are generally expensive and difficult to adapt to onboard data processing scenarios, in which low-weight and low-power integrated components are highly desirable to reduce mission payload. An exciting new development in this context is the emergence of graphics processing units (GPUs), which can now satisfy extremely high computational requirements at low cost. In this letter, we propose a GPU-based implementation of the automated morphological end member extraction algorithm, which is used in this letter as a representative case study of joint spatial/spectral techniques for hyperspectral image processing. The proposed implementation is quantitatively assessed in terms of both end member extraction accuracy and parallel efficiency, using two generations of commercial GPUs from NVidia. Combined, these parts offer a thoughtful perspective on the potential and emerging challenges of implementing hyperspectral imaging algorithms on commodity graphics hardware.
Spatial Preprocessing for Endmember Extraction

Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques available in the endmember extraction literature rely on exploiting the spectral properties of the data alone. As a result, the search for endmembers in a scene is conducted by treating the data as a collection of spectral measurements with no spatial arrangement. In this paper, we propose a novel strategy to incorporate spatial information into the traditional spectral-based endmember search process. Specifically, we propose to estimate, for each pixel vector, a scalar spatially derived factor that relates to the spectral similarity of pixels lying within a certain spatial neighborhood. This scalar value is then used to weigh the importance of the spectral information associated to each pixel in terms of its spatial context. Two key aspects of the proposed methodology are given as follows: 1) No modification of existing image spectral-based endmember extraction methods is necessary in order to apply the proposed approach. 2) The proposed preprocessing method enhances the search for image spectral endmembers in spatially homogeneous areas. Our experimental results, which were obtained using both synthetic and real hyperspectral data sets, indicate that the spectral endmembers obtained after spatial preprocessing can be used to accurately model the original hyperspectral scene using a linear mixture model. The proposed approach is suitable for jointly combining spectral and spatial information when searching for image-derived endmembers in highly representative hyperspectral image data sets.
On the incorporation of spatial information to endmember extraction: Survey and algorithm comparison

Several well-known algorithms have been used for endmember extraction and spectral unmixing of hyperspectral imagery by considering only the spectral properties of the data when conducting the search. However, it might be beneficial to incorporate the spatial arrangement of the data in the development of endmember extraction and spectral unmixing algorithms. In this paper, we provide a survey on the use of spatial information in endmember extraction and further compare six different algorithms (three of which only use spectral information) in order to substantiate the impact of using spatial-spectral information versus only spectral information when searching for image endmembers. The comparison is carried out using a synthetic hyperspectral scene with spatial patterns generated using fractals, and a real hyperspectral scene collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).
Graph-regularized coupled spectral unmixing for multisensor time-series analysis

A new methodology that solves unmixing problems involving a set of multisensor time-series spectral images is proposed in order to understand dynamic changes of the surface at a subpixel scale. The proposed methodology couples multiple unmixing problems via regularization on graphs between the multisensor time-series data to obtain robust and stable unmixing solutions beyond data modalities owing to different sensor characteristics and the effects of non-optimal atmospheric correction. A synthetic dataset that includes seasonal and trend changes on the surface and the residuals of non-optimal atmospheric correction is used for numerical validation. Experimental results demonstrate the effectiveness of the proposed methodology.
Multicore implementation of the multi-scale adaptive deep pyramid matching model for remotely sensed image classification

Artificial neural networks (ANNs) have been widely used in the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention. Unlike traditional CNNs methods, where the relevant information to classify the elements of a remotely sensed image is extracted only from the last fully-connected layer, the new adaptive deep pyramid matching (ADPM) model  takes advantage of the features from all of the convolutional layers. This model allows the optimal fusing weights for different convolutional layers be learned from the data itself. In addition, the combination of CNNs with spatial pyramid pooling (SPP-net) to create the basic deep network allows the use of images with multiple scales, which results in better learning process thanks to the complementary information. The original ADPM method is divided in two parts: the multi-scale deep feature extraction and the ADPM core. In this paper we present a computational improvement of the ADPM core, coding a parallel-multicore version. This strategy is shown to significantly enhance performance in the analysis of remotely sensed data.
Parallel Implementation of Hyperspectral Image Processing Algorithms

High computing performance of algorithm analysis is essential in many hyperspectral imaging applications, including automatic target recognition for homeland defense and security, risk/hazard prevention and monitoring, wild-land fire tracking and biological threat detection. Despite the growing interest in hyperspectral imaging research, only a few efforts devoted to designing and implementing well-conformed parallel processing solutions currently exist in the open literature. With the recent explosion in the amount and dimensionality of hyperspectral imagery, parallel processing is expected to become a requirement in most remote sensing missions. In this paper, we take a necessary first step towards the quantitative comparison of parallel techniques and strategies for analyzing hyperspectral data sets. Our focus is on three types of algorithms: automatic target recognition, spectral mixture analysis and data compression. Three types of high performance computing platforms are used for demonstration purposes, including commodity cluster-based systems, heterogeneous networks of distributed workstations and hardware-based computer architectures. Combined, these parts deliver a snapshot of the state of the art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel computing models into hyperspectral remote sensing problems.
A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed Hyperspectral Imagery

In this paper, a new joint spectral–spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence.
Real-Time Implementation of the Pixel Purity Index Algorithm for Endmember Identification on GPUs

Spectral unmixing amounts to automatically finding the signatures of pure spectral components (called endmembers in the hyperspectral imaging literature) and their associated abundance fractions in each pixel of the hyperspectral image. Many algorithms have been proposed to automatically find spectral endmembers in hyperspectral data sets. Perhaps one of the most popular ones is the pixel purity index (PPI), which is available in the ENVI software from Exelis Visual Information Solutions. This algorithm identifies the endmembers as the pixels with maxima projection values after projections onto a large randomly generated set of random vectors (called skewers). Although the algorithm has been widely used in the spectral unmixing community, it is highly time consuming as its precision asymptotically increases. Due to its high computational complexity, the PPI algorithm has been recently implemented in several high-performance computing architectures, including commodity clusters, heterogeneous and distributed systems, field programmable gate arrays, and graphics processing units (GPUs). In this letter, we present an improved GPU implementation of the PPI algorithm, which provides real-time performance for the first time in the literature.