nils-herrmann/clean_scirepeval huggingface dataset:
An Adaptive Density-Based Model for Extracting Surface Returns From Photon-Counting Laser Altimeter Data

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission of the National Aeronautics and Space Administration is scheduled to launch in 2017. This upcoming mission aims to provide data to determine the temporal and spatial changes of ice sheet elevation, sea ice freeboard, and vegetation canopy height. A photon-counting lidar onboard ICESat-2 yields point clouds resulting from surface returns and noise. In support of the ICESat-2 mission, this letter derives an adaptive density-based model that is capable of detecting the ground surface and vegetation canopy in photon-counting laser altimeter data. Based on results from point clouds generated by a first principle simulation and those observed by the Multiple Altimeter Beam Experimental Lidar, the ground and canopy returns can be reliably extracted using the proposed approach. Further study on performance assessment shows that smoother surfaces will result in improved accuracy of ground height estimation. In addition, the proposed detection approach has better performance in environments with lower noise, although the performance evaluation metric F-measure does not vary significantly over a range of noise rates (0.5-5 MHz). This proposed approach is generally applicable for surface and canopy finding from photon-counting laser altimeter data.
A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data

Observations from satellite lidar instruments have provided evidence in the remarkable changes in polar ice sheets on a global scale. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled for launch by NASA in 2017 and will monitor the elevation changes of polar ice sheets and vegetation canopy. To validate ICESat-2's approach of photon-counting laser altimetry, measurements obtained from the Multiple Altimeter Beam Experimental Lidar (MABEL) instrument are critical. In support of the ICESat-2 mission, this paper derives an algorithm for the detection of ground and vegetation canopy in photon-counting laser altimeter data. This approach uses a density-based clustering model and modifies the shape of search area. Based on results from MABEL observations, the proposed approach is seen to be robust in detecting ground and vegetation canopy as well as background noise reduction. In addition, this approach can be quickly implemented and adaptive to photon-counting lidar data sets with different point cloud densities.
3D Scene Reconstruction through a Fusion of Passive Video and Lidar Imagery

Geometric structure of a scene can be reconstructed using many methods. In recent years, two prominent approaches have been digital photogrammetric analysis using passive stereo imagery and feature extraction from lidar point clouds. In the first method, the traditional technique relies on finding common points in two or more 2D images that were acquired from different view perspectives. More recently, similar approaches have been proposed where stereo mosaics are built from aerial video using parallel ray interpolation, and surfaces are subsequently extracted from these mosaics using stereo geometry. Although the lidar data inherently contain 2.5 or 3 dimensional information, they also require processing to extract surfaces. In general, structure from stereo approaches work well when the scene surfaces are flat and have strong edges in the video frames. Lidar processing works well when the data is densely sampled. In this paper, we analyze and discuss the pros and cons of the two approaches. We also present three challenging situations that illustrate the benefits that could be derived from this data fusion: when one or more edges are not clearly visible in the video frames, when the lidar data sampling density is low, and when the object surface is not planar. Examples are provided from the processing of real airborne data gathered using a combination of lidar and passive imagery taken from separate aircraft platforms at different times.
Combined microwave and optical atmospheric remote sensing techniques: a review

Remote sensing of atmospheric parameters with space-based passive microwave and optical sensors matured from research experiments in the 1960s and 1970s to operational systems in the 1970s and 1980s. Atmospheric temperature and humidity profiles can be retrieved using the infrared carbon dioxide resonances, the microwave oxygen resonances, and the water vapor resonances in both spectral bands. Superior retrievals can be obtained by combining these sensors, taking advantage of the superior ability of microwaves to penetrate many cloud types and to respond better to low temperatures and negative lapse rates, while simultaneously taking advantage of the high spatial resolution, channel count, and sensitivity of modern infrared sensors, together with their reduced sensitivity to surface effects. Microwave and infrared soundings from satellites have been combined since their operational introduction in the 1970s, and progress continues to be made. These evolving techniques are reviewed.<<ETX>>
A microwave instrument for temperature and humidity sounding from geosynchronous orbit

Significant weather is often located in cloudy areas where infrared (IR) soundings are degraded or fail, and since numerical weather models are often hypersensitive to such baroclinic regions, microwave sounders can improve predictions by providing needed data. Both theoretical studies and data from polar weather satellites make clear the superiority of combined IR and microwave sounder systems relative to either system operating alone. To date, the size and weight of microwave sounders have limited their use to low Earth orbit. The use of higher frequencies can provide reduced antenna size so that a useful microwave sounder using a /spl sim/2-meter diameter aperture can be packaged on a geosynchronous satellite. This article describes the design of such an instrument.
Multisource Data Processing for Semi-Automated Radiometrically-Correct Scene Simulation

The digital imaging and remote sensing image generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multi-spectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Significant enhancements such as spectral polarimetric and active light detection and ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG's ability to generate scenes "on demand." To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, we have initiated a research effort that aims to reduce the man-in-the-loop requirements for several aspects of synthetic hyperspectral scene construction. Through a fusion of 3D lidar data with passive imagery, we are working to semi-automate several of the required tasks in the creation of high-resolution urban DIRSIG scenes. This paper reports on the progress made thus far in achieving these objectives.
Object Tracking Using High Resolution Satellite Imagery

High resolution multispectral satellite images with multi-angular look capability have tremendous potential applications. We present an object tracking algorithm that includes moving object estimation, target modeling, and target matching three-step processing. Potentially moving objects are first identified on the time-series images. The target is then modeled by extracting both spectral and spatial features. In the target matching procedure, the Bhattacharyya distance, histogram intersection, and pixel count similarity are combined in a novel regional operator design. Our algorithm has been tested using a set of multi-angular sequence images acquired by the WorldView-2 satellite. The tracking performance is analyzed by the calculation of recall, precision, and F1 score of the test. In this study, we have demonstrated the capability of object tracking in a complex environment with the help of high resolution multispectral satellite imagery.
Forward Modeling and Atmospheric Compensation in hyperspectral data: Experimental analysis from a target detection perspective

Taking into account atmospheric effects is crucial in target detection of airborne/satellite hyperspectral images. In regard to this, two physics-based approaches to atmospheric radiative transfer modeling are considered here: Atmospheric Compensation (AC) and Forward Modeling (FM). An experimental analysis is presented that encompasses target detection both relying upon an atmospherically compensated reflectance image and by generating predicted radiance target spaces through a forward modeling approach. Real hyperspectral imagery that embodies a very challenging, cluttered, mixed pixel detection problem is used to compare AC and FM approaches from an operational target detection perspective. On this data, detection in the radiance domain through FM has proven to be as effective as the standard AC plus reflectance domain processing. Experiments have also highlighted several aspects of FM approach (e.g. its intrinsic simplicity, flexibility, and applicability) that should be considered when performing target detection, especially for targets affected by high variability.
Techniques for Fusion of Multimodal Images: Application to Breast Imaging

In many situations it is desirable and advantageous to acquire medical images in more than one modality. For example positron emission tomography can be used to acquire functional data while magnetic resonance imaging can be used to acquire morphological data. In some situations a side by side comparison of the images provides enough information, but in other situations it may be considered a necessity to have the exact spatial relationship between the modalities presented to the observer. In order to accomplish this, the images need to first be registered and then combined (fused) to create a single image. In this paper we discuss the options for performing such fusion in the context of multimodal breast imaging.
Modeling and measurement of optical polarimetric image phenomenology in a complex urban environment

Polarimetric scene phenomenology yields a remote sensing modality that can be used in tandem with or alternative to panchromatic, multispectral, hyperspectral, or infrared intensity imagery. A synthetic image generator boasting a validated polarimetric modality is extremely valuable when testing optical polarimeter models prior to construction and flight test of real sensor hardware and software. Virtual airborne optical sensors can be modeled and placed above a complex synthetic urban scene to create spectrally varying Stokes vector output imagery. Material reflectances, scene geometries, solar and sensor positions, and varying atmospheric conditions all combine to produce spectropolarimetric sensor-reaching radiance that can be characterized by a degree and angle of polarization image. For this paper, example synthetic images were rendered with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and compared with real-life camera imagery of motor vehicles. Polarimetric phenomenology was found to be consistent between modeled and measured imagery.
Validation of landsat-8 OLI image simulation

Simulated remote sensing imagery is valuable for instrument design studies, analysis algorithm development and validation, as well as analyst training. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) suite of software is an image simulation tool developed by the Digital Imaging and Remote Sensing (DIRS) Laboratory at the Rochester Institute of Technology. DIRSIG has a long history of supporting sensor design. In this paper simulated Landsat-8 Operational Land Imager (OLI) imagery generated with DIRSIG is validated by comparison of several metrics with those obtained from analysis of real Landsat-8 OLI imagery. Comparison metrics include the data covariance matrix eigenstructure, supervised classification accuracies and NDVI product values. The results show that when evaluated by these comparison metrics the simulated imagery captures the key characteristics of images for a variety of scenes.
Image-Derived Prediction of Spectral Image Utility for Target Detection Applications

The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. Building on previous research that defines the assessment of the utility of a spectral image based on the detectability of subpixel targets, this paper examines the prediction of spectral image utility. It first reviews existing methods for predicting spectral image utility and then proposes a new approach in predicting spectral image utility for target detection applications that derive statistical parameters directly from the spectral image. This so-called image-derived approach predicts the likelihood of finding synthetically implanted subpixel targets. Using three airborne hyperspectral images, we benchmark prediction performance by comparing the predicted and assessed utilities, quantifying the accuracy of the prediction, and discussing the time savings associated with prediction. Initial results show utility calculation time savings of up to 40% for a single image, with the potential for greater savings if multiple target types are considered. This method of predicting spectral image utility offers a simple and efficient way of predicting image utility, which can be directly compared with our utility assessments.
NDVI sensitivity to atmospheric water vapor as a function of spectral bandwidth

The Normalized Difference Vegetation Index (NDVI) has been an important data product of the AVHRR series of instruments that have flown on the NOAA polar orbiting satellites. This index has been extensively used to monitor global vegetation and to study deforestation. As planning begins for follow-on versions, a proposal has been made to improve the robustness of the index by narrowing the spectral bands that are used in the NDVI calculation to reduce sensitivity to atmospheric water vapor. Without dramatic improvements in instrument design, this narrowing will also degrade the instrument's signal-to-noise ratio. In this paper, results are presented of an investigation into the sensitivity of the NDVI to water vapor as a function of the width of the spectral bands. Consideration was also given to the increase in instrument noise. This study concluded that spectral bandwidths of around 50 nanometers centered on two water vapor transmittance windows provide sufficient insensitivity to water vapor without significantly degrading the instrument's noise performance. However, it should be noted that changing the bandwidths from the current AVHRR will complicate the use of the index for long term environmental studies by changing the values obtained for similar scenes.<<ETX>>
Radiative transfer in the midwave infrared applicable to full spectrum atmospheric characterization

The compensation for atmospheric effects in the VNIR/SWIR has reached a mature stage of development with many algorithms available for application (ATREM, FLAASH, ACORN, etc.). Compensation of LWIR data is the focus of a number of promising algorithms. A gap in development exists in the MWIR where little or no atmospheric compensation work has been done yet an increased interest in MWIR applications is emerging. To obtain atmospheric compensation over the full spectrum (visible through LWIR), a better understanding of the radiative effects in the MWIR is needed. The MWIR is characterized by a unique combination of reduced solar irradiance and low thermal emission (for typical emitting surfaces), both providing relatively equal contributions to the daytime MWIR radiance. In the MWIR and LWIR, the compensation problem can be viewed as two interdependent processes: compensation for the effects of the atmosphere and the uncoupling of the surface temperature and emissivity. The former requires calculations of the atmospheric transmittance due to gases, aerosols, and thin clouds and the path radiance directed towards the sensor (both solar scattered and thermal emissions in the MWIR). A framework for a combined MWIR/LWIR compensation approach is presented where both scattering and absorption by atmospheric particles and gases are considered
Simulation of optical remote sensing systems

Research on understanding the remote-sensing process as a system and investigating the interrelated effects of various parameter is described. A system model for the simulation of remote-sensing systems is presented. The system is divided into three parts: the scene, the sensor, and the processing algorithms. Models are presented and implemented for these component systems. Validation of the system model is considered over a specific test site. Results of the simulation for various scene and sensor configurations are included. Results of applying the model to various system configurations using simulated Landsat sensors are then presented to show how the simulation can be used to investigate the interrelated effects of system parameters.
Impact of Wavelength Shift in Relative Spectral Response at High Angles of Incidence in Landsat-8 Operational Land Imager and Future Landsat Design Concepts

The Landsat program plays an important role in providing continuous long-term multispectral moderate resolution observations of the earth’s surface. There is an interest by the community to improve the temporal sampling in future generations of the sensors. One way to achieve higher sampling is to collect imagery with a wider swath. For future instruments using multilayer dielectric filters for band selection, this has the further implication that light may enter the filters on the detectors at a higher angle of incidence, which will shift the center wavelength of the spectral bandpasses. Through simulation of a forest environment this paper explored the impact of this effect on measured spectral radiance and the normalized vegetation difference index. The effect is quantified both for angles of incidence seen in the current Landsat-8 Operational Land Imager as well as for pushbroom designs with up to 13° off-axis imaging. Results indicate the effect will be significant compared to instrument noise and comparable to the limit of current requirements on filter manufacturing tolerances.
Hyperspectral remote sensing subpixel object detection performance

For nearly thirty years now, airborne and satellite hyperspectral imaging sensors have been used to collect high spatial resolution (1–30 meter) imagery of the earth's surface in hundreds of co-registered, contiguous spectral channels. These data have been shown to enable the detection of objects smaller than a pixel due to the spectral information present. However, it is not always obvious beforehand if a given object will be detectable in a given scene, as performance has been observed to depend on many factors including illumination conditions, scene spectral complexity, target variability, sensor artifacts as well as algorithm variations. Over the past fifteen years our research has been exploring ways to predict and assess performance of hyperspectral subpixel detection. Our methods have included analytical modeling tools, empirical blind tests, and quality metrics for spectral imagery. Results of this work have confirmed the feasibility of hyperspectral subpixel objection detection and have provided tools for quantification of the performance.
Linear unmixing performance forecasting

The quantitative forecasting of hyperspectral system performance is an important capability at every stage of system development including system requirement definition, system design, and sensor operation. In support of this, Lincoln Laboratory has been developing an analytical modeling tool to predict end-to-end spectroradiometric remote sensing system performance. Recently, the model has been extended to more accurately depict complex natural scenes by including multiple classes in the target pixel through the use of a linear mixing model. Additionally, a linear unmixing algorithm has been implemented to predict retrieved fractional abundances and their associated errors due to both natural variability and corrupting noise sources. This paper describes the details of this multiple target class model enhancement. Comparisons are presented between the model predictions and measured spectral radiances, as well as unmixing results obtained from data collected by NASA's EO-1 Hyperion space-based hyperspectral sensor. Additionally, results of an analysis using the enhanced model are presented to show the sensitivity of end member fractional abundance estimates to system parameters using linear unmixing techniques.
Automatic fundus image field detection and quality assessment

Fundus images are an important diagnostic tool for many retinal diseases. Sometimes the images captured are of low quality and cannot be used for diagnosis requiring repeat image acquisition. So, it is efficient to have an automatic system to assess the quality of the fundus image during the time of image capture. We have developed an automatic approach to assess the quality of the acquired fundus image based upon the inherent symmetry of retinal vessels. We approach the problem of quality assessment in two ways-individual quality assessment of a single fundus image and comprehensive quality assessment of a set of three fundus images of different fields of an eye. Our method also detects the field and side of the fundus image using the position of optic disc and the intensity information in two local windows.
Robust Extraction of Exterior Building Boundaries from Topographic Lidar Data

Methods for generating accurate building models from lidar data have received considerable attention in the recent literature. Many of the proposed techniques examine the data to define dominant planes in the structure, then intersect these planes to determine the location of internal edges and vertices. However, since most airborne lidar datasets are collected from near-nadir orientations, there are usually very few data points that lie on vertical surfaces. As such, it is usually difficult to determine the planes corresponding to exterior walls using data points on these walls. However, if we assume that exterior walls are oriented directly under the outer boundary of the roof structure, we may identify the geometry of these walls by modeling the 2D shape of the building exterior. This paper presents a robust approach for extracting this exterior boundary directly from the lidar data.
Parameter Studies for Spectral Imager Application Performance

Multi- and hyperspectral imaging systems have found utility in a variety of Earth remote sensing applications. Much research has focused on developing new ways to process the data and obtain improved results in a given application. However, relatively little research has focused on the open question of what is the optimum performance possible in a given situation. The work discussed in this paper is aimed at exploring that question through analytical modeling and performance trade studies. We show example analysis results that indicate performance floors where no further improvement is possible due to improved spatial resolution or signal-to-noise ratios in given analysis tasks.