nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic image simulations for adaptive sensor performance predictions

Comprehensive simulation of multispectral/hyperspectral synthetic imagery captured from a moving aerial platform is necessary for predicting real-life performance of adaptive sensor models. Physics-based modeling and simulation tools are required to synthesize sensor-reaching radiance imagery of an urban scene while incorporating moving vehicular targets, moving platform position & orientation with uncertainty, atmospheric conditions, and time-of-day effects. Trade studies of design parameters can be conducted to compare sensor output image and spectral quality. Orthorectification of direct detector imagery using a digital elevation map can produce registered imagery for target-tracking applications. This paper describes a novel combination of these modeling tools to support end-to-end adaptive sensor design studies.
The IEEE GRSS data and algorithm standard evaluation (DASE) website: Incrementally building a standardized assessment for algorithm performance

In order to ensure homogeneity in performance assessment of proposed algorithms for information extraction in the Earth Observation (EO) domain, standardized remotely sensed datasets are particularly useful and welcome. Fully aware of this principle, the IEEE Geoscience and Remote Sensing Society (GRSS) and especially its Image Analysis and Data Fusion Technical Committee (IADF), has been organizing for some years now the Data Fusion Contest (DFC). In the DFC, one specific dataset is made available to the scientific community, which can download it and use it to test its newly developed algorithms. The consistence of the starting dataset across participating groups ensures the significance of assessing and ranking results, to finally proclaim the winner who scored the highest. More recently, the IEEE GRSS has provided one more contribution to the standardization effort by building the Data and Algorithm Standard Evaluation (DASE) website. DASE can distribute to registered users a limited set of possible “standard” open datasets, together with some ground truth info, and automatically assess the processing results provided by the users. In this paper we report on the birth of this initiative and present some recently introduced features.
SAR and HSI data fusion for counter CC&D

There have been several examples in which both synthetic aperture radar (SAR) and hyperspectral imaging (HSI) systems collected data in support of military operations (SMO). FOPEN (foliage penetration) radar has been used to penetrate tree canopies in order to detect objects. On the other hand, spectral differences between targets and backgrounds are used in HSI systems. Both SAR and HSI systems may suffer substantial false alarm and leakage rates due to respective background clutter. It is expected that a combined SAR and HSI system will greatly enhance the detection and identification performance. Based on the features derived from SAR and HSI data, a fusion approach has been established. Data sets of SAR and HSI over a common area from the Dixie data collection (May 1997 from Vicksberg, Mississippi) are used in this paper to demonstrate the fusion approach. The site contained several camouflage nets and vehicles. One of the vehicles was covered under a camouflage net. Target detection is shown for each data set based on RCS (radar cross section) and spectral features. In particular, a transformation of the spectral measurements into principal components was used to reduce the dimensionality of HSI data as well as to facilitate spectral feature extraction and material identification. SAR and HSI detections were subsequently combined via image coregistration. The fusion results showed that false detections in the SAR image were greatly reduced with background characterization of trees from HSI and target detections were confirmed with detection of camouflage nets and material identification of vehicle paints.
Parameter trade-offs for imaging spectroscopy systems [remote sensing]

To help better understand the problems of specifying data acquisition parameters and extracting desired information from the voluminous data, research has been focused on understanding the remote sensing process as a system and investigating the interrelated effects of various parameters. A system model is used to explore system parameter trade-offs for a model sensor based on the High-Resolution Imaging Spectrometer (HIRIS). Radiometric performance was studied, along with the effect on classification accuracy of several system parameters. The atmosphere and sensor have significant effects on the mean received signal and noise performance. The effect of random uncorrelated errors in the radiometric calibration of the detector array is discussed. Accurate pixel-to-pixel relative radiometric calibration and the use of the image motion compensation (IMC) option are seen to improve classification accuracy. The selection of feature sets based on combining spectral bands was studied under a variety of observational conditions.
The role of large constellations of small satellites in emergency response situations

After Malaysian Airlines Flight 370 (MH370) disappeared over the Indian Ocean, claims were made that had the planned Planet Labs small satellite constellation been complete, the wreckage would have been found quickly. The Planet Labs system, when complete, will provide once daily images of every point on Earth at three to five meter spatial resolution. The possibility of using such a system to help in the search for aircraft downed in the ocean was examined, taking into account the probability that the image would be captured by the system and the likelihood that wreckage would be identifiable in such an image. Given the conditions of image capture and some information about the rate at which aircraft wreckage will sink, it was found that the probability of capturing an image of the wreckage was 6.92×10-4 if the wreckage sank in 30 minutes, and 1.725×10-6 if the wreckage sank in 90 seconds. If the image was captured, human analysts were able to identify the wreckage in simulated imagery with a probability of detection of 0.8 and a probability of false alarm of 0.0. Machine analysis proved less accurate, resulting in PD = 0.73 and PFA = 0.33. Given the low probability that the aircraft wreckage could be imaged using a satellite system, even under the optimistic assumption that the aircraft crashed entirely intact and sank in 30 minutes, it is unlikely that a Planet Labs-like system could have assisted in the search for MH370.
Model-based Exploration of HSI Spaceborne Sensor Requirements with Application Performance as the Metric

As an aid to the requirements analysis of future spaceborne hyperspectral imaging systems, an example study is presented which uses an analytical performance prediction model to study application performance as a function of system parameters. In particular, the forecasting and analysis of spectroradiometric system performance (FASSP) model is used to refine requirements for spatial resolution, spectral resolution, and aperture size in an unresolved road detection application. Results show roads as small as 5 meters could be detected with a system having 5 meter ground resolution, 10 nm spectral resolution, and a 0.25 meter aperture operating from 450 km altitude.
Receiver Operating Characteristic Curve Confidence Intervals and Regions

Many researchers have presented results showing the empirical performance of target detection algorithms using hyperspectral or synthetic aperture radar imagery. In nearly all cases, these probabilities of detection and false alarm are presented as precise values as opposed to their true nature as estimates of random values. In this letter, we provide analytical tools and examples of computing confidence intervals and regions around these estimates commonly presented as points on receiver operating characteristic (ROC) curves. It is suggested that these tools be adopted by researchers when presenting their results to provide their audience with a quantitative metric for proper interpretation of empirically estimated ROC curves.
Extracting Structural Vegetation Components From Small-Footprint Waveform Lidar for Biomass Estimation in Savanna Ecosystems

Measurement of vegetation biomass accumulation is critical for ecosystem assessment and monitoring, but doing so typically involves extensive field data collection that yields relatively crude structural outputs, e.g., plot- or site-level metrics. This study assessed the utility of airborne light detection and ranging (lidar) waveform features to explain structural and biomass variation in a savanna ecosystem across a land-use gradient. The ability of aboveground waveform lidar features to model field-based woody and herbaceous biomass measurements was evaluated statistically by regression models using forward variable selection. Waveform features explained 76% of the variation in woody biomass in a regulated communal land use area (RMSE = 29.0 kg). The waveform features were also correlated to herbaceous measurements in the same land-use area, with increased correlations at higher biomass levels. These results indicate that small-footprint waveform lidar data potentially can be used as a single modality to describe heterogeneous woody cover in a savanna environment; however, further research is warranted during the full growing season to fully evaluate its performance.
Measurements and modeling of LWIR spectral emissivity of contaminated quartz sand

The fundamental understanding of effects of liquid contaminants on the longwave infrared spectral emissivity of surfaces contaminated is desirable. This research describes modeling and longwave infrared spectral emissivity measurements for samples of SiO2 (sand) with and without 0.3% (by weight) of SF96 (poly dimethyl siloxane) oil. Two different sand particle size ranges were considered. The modeling was performed using a microscattering code and the empirical emissivity measurements were made outdoors using a D&P Instruments Model 102F MicroFTIR non-imaging spectrometer. The data were calibrated and processed to retrieve the spectral emissivity. General observations included a significant increase in emissivity in the 8 to 9 and 12.5 to 13 micron regions due to the presence of the SF96. The comparison between the modeled and measured emissivities shows a consistent trend and significant separability between the spectral emissivity of sand with and without the SF96 present.
Characterization of basic scattering mechanisms using laboratory based polarimetric synthetic aperture radar imaging

Understanding basic scattering mechanisms is critical to characterize objects in polarimetric synthetic aperture radar (PolSAR) imaging. For classifications from PolSAR data and modeling polarimetric signatures, characterization of the basic scattering mechanisms is required with good ground truth information. This paper discusses about a low cost laboratory based PolSAR imaging system to characterize the basic scattering mechanisms. Polarimetric SAR images with single surface reflection is captured that represent single bounce scattering. PolSAR image of a real scene is recorded and the image is overlaid on visible satellite imagery for interpretation. The main motivation behind this work is to collect polarimetric SAR data with ground truth information that represents the basic scattering mechanisms with full control over the scene and the radar.
Improved modeling of background distributions in an end-to-end spectral imaging system model

Previously, an analytical end-to-end spectral imaging system model has been developed. The model is constructed around the propagation of spectral statistics from the scene, through the sensor, and processing transformations to lead to prediction of a performance metric. In this analytical framework the description of the class statistics has been by their spectral mean vector and spectral covariance matrix (first and second order statistics). This representation is only strictly accurate when the underlying classes are Gaussian in nature. While some background classes fall into this category, many have been observed to be nonGaussian in nature. As a work-around for this limitation, we have often formed subclasses in the background, which when combined form a "composite" background class that can be multimodal. However, we have observed in estimates of empirical data distributions that unimodal backgrounds often have longer tails than those predicted by the Gaussian distribution. Recently, it has been demonstrated that a family of distributions, known as the elliptically contoured multivariate t-distributions, can provide an accurate depiction of empirically observed backgrounds. These distributions are parameterized by their multivariate mean vector and covariance matrix, but also by a degree of freedom parameter, M. By varying M, excellent fits to empirical distributions have been observed. Another key feature of these distributions is that the number of degrees of freedom has been shown to be invariant to linear transformations. Since the analytical model operates by performing a sequence of linear transformations on the statistics, the input value of M is preserved and can be used at any stage of the model to represent the class statistics. This paper describes an implementation of the elliptically contoured t-distributions to represent background classes in the end-to-end system model. The functional form and examples of the t-distributions are shown. Results are presented comparing predictions of target detection performance using backgrounds modeled by multiclass Gaussian distributions with the new elliptical-t distributions.
Feature-aided tracking via synthetic hyperspectral imagery

Hyperspectral imaging (HSI) feature-aided tracking (FAT) is an emerging area of research, employing HSI instruments and exploitation techniques with the goal to track moving objects within challenging environments and across frequent ambiguities. A series of studies have been conducted to demonstrate HSI-FAT with contemporary and novel HSI instruments. Synthesized HSI data have been the key enabler to this effort. Capabilities have been evaluated with synthetic models of low-cost, off-the-shelf sensors such as a video-rate liquid crystal tunable filter, as well as sophisticated emerging sensor concepts such as microelectromechanical-adapted systems. A suite of end-to-end synthetic experiments have been conducted, which include high-fidelity moving-target urban vignettes, synthetic hyperspectral rendering, and full image-chain treatment of the various sensor models. Corresponding algorithm development has focused on motion segmentation, spectral feature modeling, classification, fused kinematic/spectral association, and adaptive sensor feedback/ control.
Target discrimination via optimal wavelength band selection with synthetic hyperspectral imagery

Hyperspectral imaging (HSI) tracking is an emerging area of research, employing HSI instruments and exploitation techniques with the goal to track moving objects within challenging environments and across frequent ambiguities. Dimensionality reduction through wavelength band selection can help resolve such ambiguities quickly and thereby improving the feature-aided tracking performance in realtime platforms. A novel band selection algorithm is proposed to determine the optimal subset of bands that contain important information for classification. A series of studies have been conducted to evaluate the band selection algorithm and to demonstrate the benefits of optimal wavelength band selection. Synthetic HSI data using the image simulation code DIRSIG has been a key enabler to this effort. A suite of end-to-end synthetic experiments have been conducted, which include high-fidelity moving-target urban vignettes, synthetic hyperspectral rendering, and full image-chain treatment of the various sensor models.
Effects of cubesat design parameters on image quality and feature extraction for 3D reconstruction

This paper reports the initial investigation of the use of cube-satellites (cubesats) for image-based, three-dimensional (3D) reconstruction. Cubesats are emerging as a low-cost, inexpensive, and quickly deployable alternative to their larger satellite predecessors but their functionality is limited by the payload and bus electronics that can fit in the minimal volume. This paper addresses the impact of the spatial resolution limitation of state-of-the-art cubesat imagers on 3D reconstruction, which is assessed with building height and surface normal measurements. For the nadir ground-sampled distance (GSD) range of 0.25 to 2 m, reconstruction results yielded building height estimates that varied by approximately two meters and surface normal estimates with an error ranging from 1-29 degrees depending on the complexity of the surface.
Hyperspectral imaging phenomenology for the detection and tracking of pedestrians

The popularity of hyperspectral imaging in remote sensing continues to to be adapted in novel ways to overcome challenging imaging problems. This paper reports on some of the latest research efforts exploring the phenomenology of using hyperspectral imaging as an aid in detecting and tracking human pedestrians. An assessment of the likelihood of distinguishing between pedestrians given observable materials and based on signal-to-noise level is presented. Initial results indicate favorable separability can be achieved with signal-to-noise ratios as low as 13 for certain materials. Additionally, an overview of a real-world urban hyperspectral imaging data collection effort is presented.
Model-based video scene clustering with noise analysis

In content-based video analysis, scene clustering is an important step toward automated understanding of video semantics, identification of video events, and indexing and retrieval of relevant video contents. Many methods have been proposed to cluster video shots into scenes by using conventional k-means clustering and hierarchical clustering methods. However, "noise" shots analysis has not been fully investigated and incorporated in the clustering procedure. In this paper, we propose a Gaussian mixture model based clustering method incorporating noise analysis. The proposed method can identify noise shots and predict the scene types of new coming shots with satisfactory results.
Playout adaptation based packet scheduling for scalable video delivery over wireless links

In this paper, we propose an efficient packet scheduling algorithm based on a novel adaptive playout for scalable video delivery over wireless links. The proposed playout adaptation algorithm monitors the playout buffer status to adjust the playout speed by active and passive ways to prevent playout buffer from underflow and avoid annoying playout interruptions. The active playout adaptation proportionally adjusts the playout speed based on the buffer fullness. The passive playout adaptation is enabled when the network is seriously congested and adjusts the playout speed to the lowest one directly. In addition to the adaptive playout deadline, the packet priority and channel conditions are incorporated in the proposed packet scheduling algorithm. Packets are selected for transmission by maximizing the quality of the received video. The playout-deadline aware packet retransmission is also performed. The simulation results show that the proposed approach can efficiently reduce the playout latency and minimize the video quality degradation.
Gait-Based Human Age Estimation

We investigate in this paper the problem of estimating human ages from gait signatures. To our knowledge, this problem has not been formally addressed in the literature. Estimating human ages at a distance has a number of potential applications, including visual surveillance and monitoring in such public places as airports, railway stations, shopping malls, and various building entrances. Motivated by the fact that human gait appearances vary between males and females even within the same age group, we learn a multilabel-guided subspace to better characterize and correlate the age and gender information of a person for estimating human age. As human ages assume only nonnegative values and existing multilabel learning techniques mainly deal with ensembles of binary classes, we devise an effective label encoding scheme to convert each age value to a binary sequence, making conventional multilabel learning suitable for our task. To better characterize human gait appearance and enhance the robustness of the proposed age estimation method, we extract a set of over-complete Gabor features including both Gabor magnitude and Gabor phase information of a gait sequence and perform multiple feature fusion to enhance the age estimation performance. Our experimental results clearly demonstrate the feasibility of using gait signatures to estimate human age and the efficacy of our proposed method.
Uncorrelated Discriminant Nearest Feature Line Analysis for Face Recognition

We propose in this letter a new subspace learning method, called uncorrelated discriminant nearest feature line analysis (UDNFLA), for face recognition. Motivated by the fact that existing nearest feature line (NFL) can effectively characterize the geometrical information of face samples, and uncorrelated features are desirable for many pattern analysis applications, we propose using the NFL metric to seek a feature subspace such that the within-class feature line (FL) distances are minimized and between-class FL distances are maximized simultaneously in the reduced subspace, and impose an uncorrelated constraint to make the extracted features statistically uncorrelated. Experimental results on two widely used face databases demonstrate the efficacy of the proposed method.
Color image denoising using wavelets and minimum cut analysis

Wavelet thresholding has proven to be an efficient edge-preserving denoising method for grayscale images, especially when it exploits the interscale correlations of wavelet coefficients. Intrascale correlations can further improve the denoising performance, but the gain for grayscale images is generally small. In this letter, we demonstrate that the gain can become substantial in color image denoising, especially for smooth image color-difference components. We then propose a new denoising method, based on the minimum cut algorithm, to exploit both the interscale and intrascale correlations of wavelet coefficients. The proposed method achieves up to 5-dB gain in peak signal-to-noise ratio for color-difference images and leads to fewer visual color artifacts.
Frame rate up-conversion with edge-weighted motion estimation and trilateral interpolation

We propose in this paper a novel video frame-rate up-conversion method using edge-weighted motion estimation and trilateral filtering. First, the method produces two frames of the intermediate frame from its previous and following frames with estimated motion vectors. It then obtains an initial estimate from the two frames and calculates its pixel reliability. Finally, a trilateral filter is applied on the initial estimate to correct the unreliable pixels and missing pixels. Compared with the existing methods, the proposed method not only reduces the computation cost on motion vector estimation, but also suppresses the interpolation noises and motion compensation errors. Experimental results show that the proposed method provides better subjective and objective quality, and obtains up to 4-dB PSNR improvement.