nils-herrmann/clean_scirepeval huggingface dataset:
Towards robust forest leaf area index assessment using an imaging spectroscopy simulation approach

Few studies have evaluated how per-pixel structural configurations could impact spectral response. This has an impact on how we assess especially large area/global ecosystems. In an effort to understand this impact of sub-pixel structural variation on large-footprint imaging spectroscopy, a simulation approach was used, which provides precise knowledge of target geometry and radiometry. We demonstrated the validity of the proposed simulation in terms of one such structural metric of interest, namely leaf area index (LAI). LAI is a key vegetation structural parameter, which has implications for predicting ecosystems' foliar spatial distribution, health, photosynthesis, transpiration, and energy transfer. Simulated LAI measurements were validated with field data obtained from AccuPAR measurements (R2 = 0.76) and by comparison to NDVI data obtained from simulated AVIRIS imagery (R2 = 0.92-0.65, depending on sampling interval). These data were used to propose an appropriate sampling protocol for LAI data collection, thus providing for efficient data collection, while minimizing variability of individual measurements. These efforts will support preparatory science experiments towards understanding the phenomenology of NASA's next-generation imaging spectrometer, HyspIRI.
Error analysis of spectral reflectance derived from imaging spectrometer data

As the field of remote imaging spectrometry grows, interest increases in applications that require the data be "corrected" to surface reflectance. However, the utility of the data for these applications will be limited by the accuracy to which the correction can be performed. Two approaches to atmospheric compensation are reviewed and applied to airborne spectrometer data to study their error characteristics. An end-to-end analysis model is used to extend the study to examine the effects of individual sources of error. Results indicate that random errors of 1 to 2% reflectance units and bias errors of 1 to 4% are achievable in atmospheric window regions, with considerably higher errors in atmospheric absorption bands.
Atmospheric Compensation using a Geometrically-Compensated Empirical Line Method

This paper presents a geometric extension to the well known Empirical Line Method (ELM) for atmospheric compensation, enabling improved results through the use of 3D scene geometry. Typically, this geometry is assumed to be known, either through direct measurement or analysis of other imagery such as lidar or photogrammetric products. However, when the scene geometry is unknown and only spectral information is available, in certain cases estimates of the surface orientation may be obtained directly from image brightness values. In this paper we derive a means to determine surface orientation estimates from a pair of brightness images, and then use this geometric information to improve the ELM solution.
Spectral image utility prediction

The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application task. The robust ability to predict the utility of an image for a given application would facilitate sensor design trade studies, provide a basis for tasking image collection activities, and create the foundation for an image archive indexing scheme. In this paper, we examine methods for predicting the utility of spectral images for detecting sub-pixel targets using the constrained energy minimization matched filter detector. The result of our initial work is a prediction of the likelihood of finding a synthetically implanted target in a target-free image in advance of actually applying the detector. We define image utility for the target detection application as the probability of detection at a specified probability of false alarm. We analytically predict this utility for a given image by first estimating statistical parameters directly from the image, then operating on these parameters with the matched filter detector. Three parametric statistical models are used for characterizing the image background: the global Gaussian, the multiple-class Gaussian, and the elliptically contoured t-distributions. The target models come from a library of target materials and are assumed to be multivariate Gaussian with known mean and covariance. We benchmark prediction performance by comparing predicted detection probabilities to empirical results obtained by applying the detector to the data for two HYDICE images. Our longer term objective is to build on this initial result by developing a more general spectral image quality and utility framework, and specific metrics for the prediction of utility across many potential applications.
Unmixing analysis: model prediction compared to observed results

The quantitative forecasting of spectral imaging system performance is an important capability. The ability to accurately predict the effects on utility of the data due to scene conditions, sensor performance, or even algorithm parameters, can be very important. To this end, an analytical modeling tool has been under development to predict end-to-end spectroradiometric remote sensing system performance, and to understand the relative impact of various system parameters on that performance. Recently, data were collected by NASA's EO-1 Hyperion spaced-based hyperspectral imager over an area in Southern California including spatially resolved buildings of known size. The area of interest was also imaged with previous low-altitude overflights of NASA/JPL's AVIRIS airborne imaging spectrometer. The AVIRIS data provided an opportunity to investigate the accuracy of unmixing analysis applied to the Hyperion image as well as to serve as a source of input data in model forecasts. This paper describes the results of analysis of the remotely sensed data as well as comparisons to predictions made by the analytical performance prediction model. While the empirical analyses provide point results in terms of the abundance of the buildings per pixel, the model predicts the anticipated variation in the abundance estimates given inherent variability of the building roof material and nearby backgrounds. The model is also exercised to show the impact on the abundance estimates from various remote sensing system parameters including sensor noise, radiometric calibration error, and the number of endmembers assumed in the unmixing algorithm. In the example studied, the natural surface variability and the use of endmembers in the unmixing that were not present in the scene were found to have the most impact on the abundance estimates.
Spectral Image Utility Sensitivity to Image Preprocessing and Information Exploitation Parameters

For a wide range of application areas, quantifying the ability of a spectral image to satisfy the informational requirements of an application task would be desirable. We propose that this metric may be termed the "image utility" and present a method for assessing the utility of spectral images for the subpixel target detection task. In this paper, we investigate the sensitivity of this utility metric to various image chain parameters. In particular, we examine the effect of small variations of preprocessing and target detection scenario parameters on the assessed utility of a spectral image. We offer a method of quantifying the sensitivity to facilitate a rank ordering of parameter sensitivities. This exploration constitutes an initial step towards gaining a fuller understanding of the key parameters that drive spectral image utility.
A combined approach for ice sheet elevation extraction from lidar point clouds

Understanding the total mass balance of Earth's polar cryosphere is a significant aspect of estimating sea level rise due to climate change. Measuring ice sheet elevation and ground height is essential for determining the total glacier mass balance. Satellite-based and airborne photon-counting lidar sensors, such as the upcoming Ice, Cloud and land Elevation Satellite 2 (ICESat-2), will provide accurate measurements of local topographical changes along space and time. Point clouds obtained from DIRSIG simulations are critical for scientists to develop algorithms that will be used for the upcoming ICESat-2 mission because they provide area truth for comparison. Three previous elevation-retrieval algorithms are reviewed and a new combined approach is presented. Results show that the combined approach can improve the accuracy when compared with only one of the previous three approaches.
Hyperspectral target detection using multiple platform cuing

Hyperspectral imaging has been demonstrated to achieve unresolved object detection through use of the spectral information. However, in many cases, these demonstrations have been in near ideal situations where the use of laboratory spectra with pristine data has lead to success. Complexities introduced in real-world situations such as a cluttered urban environment make successful detection challenging. One approach to improving performance is to use the synergistic effects of multiple sensors surveying a common area. These multiple sensors can be used to cue each other and enhance detection or tracking of objects. For maximum robustness, however one would want to minimize the complexity of processing algorithms such as those used to compensate for atmospheric and illumination effects. This paper investigates the limits of the use of spectra observed under one set of conditions to be used to detect an object under a different set of conditions. The results indicate good performance can be achieved across a reasonable range of illumination and viewing conditions.
Operational and Performance Considerations of Radiative-Transfer Modeling in Hyperspectral Target Detection

Accounting for radiative transfer within the atmosphere is usually necessary to accomplish target detection in airborne/satellite hyperspectral images. In this paper, two methods of accounting for the illumination and atmospheric effects-atmospheric compensation (AC) and forward modeling (FM)-are investigated in their application to target detection. Specifically, several crucial aspects are examined, such as the processing required, the computational complexity, and the flexibility accorded to an imperfect knowledge of acquisition conditions. Real ground-truthed hyperspectral data are employed in order to evaluate the operational applicability of such approaches in a target-detection scenario, as well as their impact on the processing-chain computational complexity. Results indicate that AC is recommended when accurate knowledge of the acquisition conditions is available, and the image has relatively uniform illumination and nonshadowed targets. Conversely, FM is preferred if scene conditions are not well known and when the targets may be subject to varying illumination conditions, including shadowing.
An introduction to abundance map reference data, with applications in spectral unmixing

Reference data (“ground truth”) maps are commonly used to quantitatively assess the performance of imaging spectrometer classification algorithms. However, standard reference data scenes typically are not sufficiently detailed to support assessment of spectral unmixing algorithms. Furthermore, commonly used reference data often lack validation reports that estimate error in the reference data itself, and new reference data are prohibitively expensive to generate using traditional methods. This paper presents a summary of our work, which is focused on introducing new methodologies to efficiently generate and validate abundance map reference data (AMRD), which can then be applied to assess the performance of spectral unmixing on real remotely sensed imagery. AMRD, generated using our methodology, had a validated mean and standard deviation error of 3.0% and 6.3%, respectively, which rivaled the accuracy the best traditional methods. A separate experiment designed to replicate our methodology, using different scenes and imagery, confirmed the relative accuracy of our techniques.
Full spectrum modeling of at-sensor spectral radiance variability due to surface variability

In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to cover the visible and near infrared through longwave infrared portion of the optical spectrum (0.4 to 14 /spl mu/m). The model takes statistical descriptions of surface spectral reflectances and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated. This paper presents the theory for analytically transforming surface statistics to at-sensor spectral radiance statistics for a downward-looking hyperspectral sensor observing both reflected sunlight and thermally emitted radiation. Comparisons of the model's predictions with measurements from an airborne hyperspectral sensor are presented. An example is included to show the model's utility in understanding the magnitude of full spectrum radiance components.
Hyperspectral linear unmixing: Quantitative evaluation of novel target design and edge unmixing technique

Remotely sensed hyperspectral images (HSI) have the potential to provide large amounts of information about a scene. HSI, in this context, are images of the Earth collected with a spatial resolution of 1m to 30m in dozens to hundreds of contiguous narrow spectral bands over different wavelengths so that each pixel is a vector of data. Spectral unmixing is one application which can utilize the large amount of information in HSI. Unmixing is a process used to retrieve a material's spectral profile and its fractional abundance in each pixel since a single pixel contains a mixture of material spectra. Unmixing was used with images collected during an airborne hyperspectral collect at the Rochester Institute of Technology in 2010 with 1m resolution and a 390nm to 2450nm spectral range. The goal of our experiment was to quantitatively evaluate unmixing results by introducing a novel unmixing target. In addition, a single-band, edge unmixing technique is introduced with preliminary experimentation which showed results with mean unmixing fraction error of less than 10%. The results of the methods presented above helped in the design of future collection experiments.
A Low-Cost Laboratory-Based Polarimetric Synthetic Aperture Radar System for Scattering Analysis [Education Corner]

The scattering mechanisms captured using polarimetric synthetic aperture radar (PolSAR) imaging enable applications like target detection and image classification. This article presents PolSAR images from a unique low-cost, laboratory-based imaging system that captures the basic scattering mechanisms using radar calibration targets, such as a flat plate and dihedral and trihedral corner reflectors. The characterization of the basic scattering mechanisms with good ground truth information is important for classification with PolSAR imagery and for modeling polarimetric signatures. The PolSAR instrument is calibrated using a corner reflector, and the calibration is then validated using an independent data set. The calibration is done in terms of radar cross section (RCS) in decibels referenced to a square meter (dBsm). Results show the system is capable of capturing the basic scattering mechanisms of the single and double bounce. Validation results of the calibration process are off by about 3 dBsm and 4.5 dBsm from the theoretical values of the RCS for the vertical transmit-vertical receive (VV) and horizontal transmit-horizontal receive (HH) channel calibration, respectively. This is acceptable for a low-cost system capable of differentiating scattering mechanisms. This PolSAR system can be used for experimentation within the laboratory for phenomenology studies.
Full-spectrum spectral imaging system analytical model

In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to cover the visible through longwave infrared portion of the optical spectrum (0.4-14 /spl mu/m). The model uses statistical descriptions of surface spectral reflectances/emissivities and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. This work presents theory for the analytical transformation of surface statistics to at-sensor spectral radiance statistics for a downward-looking hyperspectral sensor observing both reflected sunlight and thermally emitted radiation. Comparisons of the model predictions with measurements from an airborne hyperspectral sensor are presented. Example parameter trades are included to show the utility of the model for applications in sensor design and operation.
Spectral imaging system analytical model for subpixel object detection

Data from multispectral and hyperspectral imaging systems have been used in many applications including land cover classification, surface characterization, material identification, and spatially unresolved object detection. While these optical spectral imaging systems have provided useful data, their design and utility could be further enhanced by better understanding the sensitivities and relative roles of various system attributes; in particular, when application data product accuracy is used as a metric. To study system parameters in the context of land cover classification, an end-to-end remote sensing system modeling approach was previously developed. In this paper, we extend this model to subpixel object detection applications by including a linear mixing model for an unresolved object in a background and using object detection algorithms and probability of detection (P/sub D/) versus false alarm (P/sub FA/) curves to characterize performance. Validations with results obtained from airborne hyperspectral data show good agreement between model predictions and the measured data. Examples are presented which show the utility of the modeling approach in understanding the relative importance of various system parameters and the sensitivity of P/sub D/ versus P/sub FA/ curves to changes in the system for a subpixel road detection scenario.
An Analytical Model for Optical Polarimetric Imaging Systems

Optical polarization has shown promising applications in passive remote sensing. However, the combined effects of the scene characteristics, the sensor configurations, and the different processing algorithm implementations on the overall system performance have not been systematically studied. To better understand the effects of various system attributes and help optimize the design and use of polarimetric imaging systems, an analytical model has been developed to predict the system performance. The model propagates the first- and second-order statistics of radiance from a scene model to a sensor model and, finally, to a processing model. Validations with data collected from a division of time polarimeter are presented. Based on the analytical model, we then define a signal-to-noise ratio of the degree of linear polarization and receiver operating characteristic curves as two different system performance metrics to evaluate the polarimetic signatures of different objects, as well as the target detection performance. Several examples are presented to show the potential applications of the analytical model for system analysis.
Exploring limits in hyperspectral unresolved object detection

Hyperspectral imaging systems have been shown to enable unresolved object detection through enhanced spectral characteristics of the data. Robust detection performance prediction tools are desirable for many reasons including optimal system design and operation. The research described in this paper explores the general understanding of system factors that limit detection performance. Examples are shown for detectability limits due to target subpixel fill fraction, sensor noise, and scene complexity.
Comparison of radiative transfer in physics-based models for an improved understanding of empirical hyperspectral data

This paper examines the methodology of detecting targets in airborne or satellite hyperspectral imagery using physicsbased models. More specifically, the radiative transfer inherently coupled to various physical models is considered. In fact, taking into account atmospheric effects is crucial in target detection applications, especially when dealing with targets that are particularly difficult to detect. Many tools have been developed independently which incorporate physical models that simulate atmospheric radiation transfer. Some (e.g. DIRSIG) predict sensor-reaching radiance while others (e.g. FLAASH, ATREM) retrieve ground-leaving reflectance by removing atmospheric effects. With the final aim of performing forward modeling target detection on a particularly challenging scenario, this paper illustrates the preliminary study carried out in order to assess the physical model employed and achieve a better data understanding before proceeding to detection. A cross-comparison between some well-known and established models, in addition to forward modeling, is examined. Results reveal the need for better understanding of real data by identifying the major sources of uncertainty. The strong impact of atmospheric condition uncertainty and adjacency effects, along with, though to a lesser extent, further inaccuracy introduced by possible calibration and spectral library measurement errors, are all factors that will be investigated in future work.
First principles modeling for lidar sensing of complex ice surfaces

Lidar sensing has been found to be a useful method of monitoring the dynamics and mass balance of glaciers, ice caps, and ice sheets. However, it is also known that ice surfaces can have complex 3-dimensional structure, which can challenge their accurate retrieval with lidar sensing. In support of future lidar sensing satellite missions, such as the upcoming ICESat-2, a joint research project was recently initiated between the Rochester Institute of Technology (RIT) and the University at Buffalo to study lidar sensing of complex ice surfaces. This effort is supported by NASA's Remote Sensing Theory program and is aimed at advancing the science of lidar sensing. The general approach is to 1) define realistic complex ice surfaces, 2) render lidar image simulations, and 3) compare the resulting data to the known surfaces to gain insight into the phenomenology of lidar sensing of snow and ice. The project will build on existing scientific understanding of light scattering from snow and ice as well as lidar sensor system modeling with a systems engineering end-to-end perspective. Initial results show the simulations capturing realistic scattering of photons in snow volumes and the resulting point clouds measured by a model spaceborne lidar system.
Development of a web-based blind test to score and rank hyperspectral classification algorithms

Remotely sensed hyperspectral imagery plays an important role in land cover classification by supplying the user with additional spectral data as compared to high-resolution color imagery. The web application described in this paper enables users to test their classification algorithms without the risk of bias by withholding the majority of the true classification data and only providing a small section of the truth data to be used for training user algorithms. After downloading the dataset, users run their classification algorithms and upload their results back to the web application. The blind test site automatically scores and ranks the uploaded result. The Classification Blind Test web application can be found at: http://dirsapps.cis.rit.edu/classtest/.