nils-herrmann/clean_scirepeval huggingface dataset:
Fusion of hyperspectral and lidar data using generalized composite kernels: A case study in Extremadura, Spain

The light detection and ranging (LiDAR) data provides very valuable information about the height of the surveyed area which can be used as a source of complementary information for the classification of hyperspectral data, in particular when it is difficult to separate complex classes. In this work, we suggest to exploit the generalized composite kernel strategy for fusion and classification of hyperspectral and LiDAR data. Our experimental results, conducted using a hyperspectral image and a LiDAR derived intensity image collected over a rural area in Extremadura, Spain, indicate that the proposed framework for the fusion of hyperspactral and LiDAR data provides significant classification results.
Binary partition tree-based local spectral unmixing

The linear mixing model (LMM) is a widely used methodology for the spectral unmixing (SU) of hyperspectral data. In this model, hyperspectral data is formed as a linear combination of spectral signatures corresponding to macroscopically pure materials (endmembers), weighted by their fractional abundances. Some of the drawbacks of the LMM are the presence of multiple mixtures and the spectral variability of the endmembers due to illumination and atmospheric effects. These issues appear as variations of the spectral conditions of the image along its spatial domain. However, these effects are not so severe locally and could be at least mitigated by working in smaller regions of the image. The proposed local SU works over a partition of the image, performing the spectral unmixing locally in each region of the partition. In this work, we first introduce the general local SU methodology, then we propose an implementation of the local SU based on a binary partition tree representation of the hyperspectral image and finally we give an experimental validation of the approach using real data.
Segmentation as postprocessing for hyperspectral image classification

Hyperspectral imaging is a technique in remote sensing that collect hundreds of images at differents wavelength values in 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 400 and 2500 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 due to improve the performance of the classification techniques. In previous works have been used similar techniques using spectral and spatial information separately or simultaneously. In this work we have focused on a region-growing segmentation algorithm, applied as postprocessing on the standard classification, produces by a SVM classifier, in order to improve the performance of the classification technique. Experimental results with a real hyperspectral data set over the city of Pavia are included.
Distributed Computing for Efficient Hyperspectral Imaging Using Fully Heterogeneous Networks of Workstations

Hyperspectral imaging is a new technique which has become increasingly important in many remote sensing applications, including automatic target recognition for military and defense/security deployment, risk/hazard prevention and response including wild land fire tracking, biological threat detection, monitoring of oil spills and other types of chemical contamination, etc. Hyperspectral imaging applications generate massive volumes of data and require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. Although most currently available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of workstations represent a very promising cost-effective solution expected to play a major role in the design of highperformance computing platforms for many on-going and planned remote sensing missions. This paper explores innovative techniques for mapping hyperspectral analysis algorithms onto heterogeneous networks of workstations available at NASA’s Goddard Space Flight Center and University of Maryland. Experimental results reveal that heterogeneous networks of workstations represent a source of computational power that is both accessible and applicable in hyperspectral imaging studies.
Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI

The development of efficient techniques for transforming the massive volume of remotely sensed hyperspectral data collected on a daily basis into scientific understanding is critical for space-based Earth science and planetary exploration. Although most available parallel processing strategies for hyperspectral image analysis assume homogeneity in the computing platform, heterogeneous networks of computers represent a promising cost-effective solution expected to play a major role in many on-going and planned remote sensing missions. To address the need for cost-effective parallel hyperspectral imaging algorithms, this paper develops an innovative heterogeneous parallel algorithm for spatial/spectral morphological analysis of hyperspectral image data. The algorithm has been developed using heterogeneous MPI (HeteroMPI), an extension of MPI for programming high-performance computations on heterogeneous networks of computers. Experimental results are presented and discussed in the context of a realistic application, based on hyperspectral data collected by NASA 's Jet Propulsion Laboratory
A New Preprocessing Technique for Fast Hyperspectral Endmember Extraction

Hyperspectral image processing represents a valuable tool for remote sensing of the Earth. This fact has led to the inclusion of hyperspectral sensors in different airborne and satellite missions for Earth observation. However, one of the main drawbacks encountered when dealing with hyperspectral images is the huge amount of data to be processed, in particular, when advanced analysis techniques such as spectral unmixing are used. The main contribution of this letter is the introduction of a novel preprocessing (PP) module, called SE2PP, which is based on the integration of spatial and spectral information. The proposed approach can be combined with existing algorithms for endmember extraction, reducing the computational complexity of those algorithms while providing similar figures of accuracy. The key idea behind SE2PP is to identify and select a reduced set of pixels in the hyperspectral image, so that there is no need to process a large amount of them to get accurate spectral unmixing results. Compared to previous approaches based on similar spatial and spatial-spectral PP strategies, SE2PP clearly outperforms their results in terms of accuracy and computation speed, as it is demonstrated with artificial and real hyperspectral images.
Nonnegative sparse autoencoder for robust endmember extraction from remotely sensed hyperspectral images

Endmember extraction is a fundamental task in spectral unmixing of remotely sensed hyperspectral images. In this work, we develop a new robust algorithm for endmember extraction which is based on a nonnegative sparse autoencoder. The proposed approach is based on two main steps. First, it uses an automatic sampler approach with local outlier factor and affinity propagation to intelligently gather a set of training samples. Then, a set of endmember signatures are extracted from the selected training samples by the nonnegative sparse autoencoder. Taking advantage from both automatic sampling and nonnegative sparse autoencoding, the proposed method can tackle problems with outliers. The effectiveness of the proposed method is verified by using simulated data. In our comparison with other state-of-the-art endmember extraction methods, the proposed approach demonstrates highly competitive performance.
Spatial-Spectral Preprocessing Prior to Endmember Identification and Unmixing of Remotely Sensed Hyperspectral Data

Spectral unmixing amounts at estimating the abundance of pure spectral signatures (called endmembers) in each mixed pixel of a hyperspectral image, where mixed pixels arise due to insufficient spatial resolution and other phenomena. A challenging problem is how to automatically identify endmembers, as the presence of mixed pixels generally prevents the localization of pure spectral signatures in transition areas between different land-cover classes. A possible strategy to address this problem is to guide the endmember identification process to spatially homogeneous areas, expected to contain the purest signatures available in the scene. For this purpose, several spatial preprocessing methods have been used prior to endmember identification. However, the preprocessing methods developed thus far only exploit the spatial information and relegate the use of spectral information to the subsequent endmember identification stage. In this paper, we develop a new spatial-spectral preprocessing (SSPP) module which can be used prior to endmember identification and spectral unmixing. The method first derives a spatial homogeneity index for each pixel in the hyperspectral image. This index is relatively insensitive to the noise present in the data. At the same time, it performs unsupervised clustering to identify a set of clusters in spectral space. Finally, it fuses spatial and spectral information by selecting a subset of spatially homogeneous and spectrally pure pixels from each cluster. These pixels constitute the new set of candidates for endmember identification. An innovative contribution of this paper is the combination of spatial and spectral information at the preprocessing stage. Another contribution is the combination, for the first time in the literature, of preprocessing techniques with endmember identification algorithms that do not assume the presence of pure signatures in the scene. An experimental comparison of the proposed method in combination with different endmember identification techniques is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that endmember identification techniques (with and without the pure signature assumption) can greatly benefit from the proposed preprocessing approach, which considers both spatial and spectral information.
Polymer Microoptoelectromechanical Systems: Variable Optical Attenuators and Accelerometers

The design, fabrication and characterization of a polymer microoptoelectromechanical system (MOEMS) based in the modulation of the total losses, is presented in this work. Three waveguides are defined on the structure: two at the edges and the third in the middle of the seismic mass. When a horizontal displacement is produced, there is an increase of the losses due to the misalignment between the waveguides. This misalignment is due either to an acceleration (obtaining an acceleration sensor) or using Aluminum as a heater electrode. When a dc voltage is applied, the mechanical beams bend, resulting in a displacement of the seismic mass. In this case, a variable optical attenuator (VOA) is obtained. Experimental results show optical sensitivities of 13.1 dB/g for negative and 17.5 dB/g for positive accelerations when characterized as an accelerometer and a power consumption of only 12 mW at 20 dB when used as a VOA, in both cases using a working wavelength of 633 nm emitted from an LED.
FIB and DRIE combination for nanotip fabrication

The authors have studied the fabrication of high-aspect ratio silicon tips by a combination of deep reactive ion etching and focused ion beam. The reactive ion etching is used to obtain so-called "rocket tips" which could be fabricated with a high aspect ratio. The rocket tips were further processed by using a focused ion beam to obtain nanotips at their apex. Typical results obtained were nanotips with a basis radius of 200 nm and a height of 2.5 /spl mu/m, with an apex radius of 5 nm, located on top of a 3 /spl mu/m wide and 9 /spl mu/m high silicon column. The process would allow however obtaining column heights of several tens of microns.
Surface quad beam polymer optical accelerometer

The design, simulation and characterization of a quad beam polymer optical accelerometer is presented. The working principle is based in the modulation of the intensity, by way of causing a misalignment between the waveguides located at the frame and the waveguide placed at the mass. A mechanical sensitivity of 34.1 /spl mu/m/g has been simulated. Stable and robust technology that only comprises two photolithographic steps is also presented. Static characterization was performed by tilting the accelerometer, showing a periodical response, with an experimental optical sensitivity higher than 6dB/g.
HYPER-I-NET: European research network on hyperspectral imaging

This paper addresses the main goals and objectives of the Hyperspectral Imaging Network (HYPER-I-NET), a recently started Marie Curie Research Training Network. The project is designed to build an interdisciplinary research community focusing on hyperspectral imaging activities. The core strategy of the network is to create a powerful interdisciplinary synergy between different domains of expertise closely related to hyperspectral imaging activities in Europe, ranging from sensor design and flight operation to data collection, processing, interpretation, and dissemination. Our main goals in this paper are to present the project to the Geoscience and Remote Sensing community and to provide an overview of the planned activities in each sub-activity covered by the network.
Impervious surface extraction from multispectral images using morphological attribute profiles and spectral mixture analysis

Morphological attribute profiles (MAPs) are one of the most effective methodologies to characterize the spatial information in remote sensing images. This technique extracts components able to accurately describe objects in the surface of the Earth. In this work, we present a new method for impervious surface extraction from multispectral images using morphological attribute profiles. The proposed method first uses morphological profiles to extend Landsat ETM+ images with additional features. Then, we adopt a vegetation-impervious surface-soil (V-I-S) model and extract three pure classes (endmembers) from these images (i.e. vegetation, impervious surface and soil) using the vertex component algorithm (VCA). Finally, linear spectral mixture analysis (SMA) is conducted to extract the impervious surface percentage (ISP). To test the performance of the proposed method, more than 300 test samples including business districts, residential areas and urban roads are randomly selected from QuickBird imagery with very high resolution. The coefficient of determination R2 is 0.7571, which significantly outperformed other standard techniques in the literature. The obtained experimental results demonstrate that the proposed approach based on morphological attribute profiles can lead to very good extraction and characterization of impervious surfaces.
A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution

Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details than those captured by the original acquisition instrument. Therefore, SR techniques are particularly useful to cope with the increasing demand remote sensing imaging applications requiring fine spatial resolution. Even though different machine learning paradigms have been successfully applied in SR, more research is required to improve the SR process without the need of external high-resolution (HR) training examples. This paper proposes a new convolutional generator model to super-resolve low-resolution (LR) remote sensing data from an unsupervised perspective. That is, the proposed generative network is able to initially learn relationships between the LR and HR domains throughout several convolutional, downsampling, batch normalization, and activation layers. Then, the data are symmetrically projected to the target resolution while guaranteeing a reconstruction constraint over the LR input image. An experimental comparison is conducted using 12 different unsupervised SR methods over different test images. Our experiments reveal the potential of the proposed approach to improve the resolution of remote sensing imagery.
Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles

Supervised classification plays a key role in terms of accurate analysis of hyperspectral images. Many applications can greatly benefit from the wealth of spectral and spatial information provided by these kind of data, including land-use and land-cover mapping. Conventional classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependencies of the adjacent pixels. To overcome these limitations, classifiers need to use both spectral and spatial information. In this paper, a framework for automatic spectral-spatial classification of hyperspectral images is proposed. In order to extract the spatial information, Extended Multi-Attribute Profiles (EMAPs) are taken into account. In addition, in order to reduce the redundancy of features and address the so-called curse of dimensionality, different supervised feature extraction (FE) techniques are considered. The final classification map is provided by using a random forest classifier. The proposed automatic framework is tested on two widely used hyperspectral data sets; Pavia University and Indian Pines. Experimental results confirm that the proposed framework automatically provides accurate classification maps in acceptable CPU processing times.
Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations

This work describes sequences of extended morphological transformations for filtering and classification of high-dimensional remotely sensed hyperspectral datasets. The proposed approaches are based on the generalization of concepts from mathematical morphology theory to multichannel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Extended morphological transformations, characterized by simultaneously considering the spatial and spectral information contained in hyperspectral datasets, are applied to agricultural and urban classification problems where efficacy in discriminating between subtly different ground covers is required. The methods are tested using real hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer and the German Aerospace Agency Digital Airborne Imaging Spectrometer (DAIS 7915). Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately.
Towards the Definition of a Flexible Hyperspectral Processing Chain: Preliminary Case Study Using High-Resolution Urban Data

In this paper, we describe a first approximation to the relevant issue of defining a part of the hyperspectral processing chain in a flexible manner. An ultimate goal of our study is to objectively quantify the impact of different (standard and new) processing stages on the generation of a realistic, user-oriented product in the context of an urban land cover mapping problem by means of hyperspectral data, selected in this work as an application case study for demonstration purposes. Although the proposed study is linked to a specific application domain, our experimental results reveal interesting considerations that may help image analysts in defining customized processing chains based on parameters which can be identified and objectively evaluated a priori, such as available sensor resolution or ancillary information. In addition, our study also demonstrates the importance of incorporating information related to both the spatial and the spectral domain in the different steps that comprise the hyperspectral processing chain; particularly when such chain can take advantage of the combined use of both sources of information as it is the case in the considered urban characterization application.
On the use of cluster computing architectures for implementation of hyperspectral image analysis algorithms

Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied by systems like the airborne visible infra-red imaging spectrometer (AVIRIS), developed by NASA Jet Propulsion Laboratory, allows their exploitation in diverse applications, such as detection and control of wild fires and hazardous agents in water and atmosphere, detection of military targets and management of natural resources. Even though the above applications require a response in real time, few solutions are available to provide fast and efficient analysis of these types of data. This is mainly caused by the dimensionality of hyperspectral images, which limits their exploitation in analysis scenarios where the spatial and temporal requirements are very high. In the present work, we describe a new parallel methodology which deals with most of the previously addressed problems. The computational performance of the proposed analysis methodology is evaluated using two parallel computer systems, a SGI Origin 2000 shared memory system located at the European Center of Parallelism of Barcelona, and the Thunderhead Beowulf cluster at NASA's Goddard Space Flight Center.
Automated selection of results in hierarchical segmentations of remotely sensed hyperspectral images

The hierarchical image segmentation (HSEG) algorithm is a hybrid of hierarchical step-wise optimization and constrained spectral clustering. Unlike most other segmentation approaches, HSEG produces a hierarchical set of image segmentations. A single segmentation level can be selected out of the segmentation hierarchy by examining how the features or individual regions change throughout the different levels of detail. Subsequently, the selection of a single segmentation result for each region can effectively transform the segmentation hierarchy into a region-adaptive segmentation approach. The above task has previously been accomplished using supervised and time-consuming procedures. This paper presents a first step towards the automation of this process, where spatial, spectral and joint spectral/spatial features are used to investigate how regions change from one hierarchical level to the next for region identification in remotely sensed hyperspectral data sets. Comparative results are presented using Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) data collected over the Salinas Valley in California.