Mixed Scenes Research Articles

Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm.

Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R2cv = 0.68, RMSEcv = 5.18 g·kg-1, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R2cv = 0.69, RMSEcv = 4.15 g·kg-1, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images

High-spatial resolution urban scenes, which contain ecological, social, and environmental zones, are composed of diverse functional areas. Depicting urban scenes from both qualitative and quantitative aspects, i.e., scene classification and scene unmixing, are increasingly important tasks. The purpose of scene classification is to mark a scene as a separate category, and this approach is widely applied to qualitatively define scenes. However, with the rapid development of cities, urban scenes usually show a mixed nature, resulting in urban mixed scenes (UMSs). Traditional scene classification cannot describe the mix of urban scenes. Few studies have focused on the quantitative measurements of UMSs, which aim at discovering and analyzing the proportion of different subscenes. In this article, a scene unmixing framework based on nonnegative matrix factorization for UMSs (UnUMS) is proposed to estimate the mixing ratio of urban scenes. In the UnUMS, a deep feature representation is used for the high-dimensional representation of the image, and then a nonnegative matrix factorization strategy is used to estimate the proportion of UMSs. Finally, the UMS can be described by the proportion of each subscene. To confirm the robust generalizability of UnUMS, the WH-center Mix dataset with a set of small mixed scenes and the WH-hy LMix dataset with a large UMS image are utilized. The root-mean-square error and the newly proposed vector angular distance are utilized to quantitatively evaluate the effect of scene unmixing from different aspects. Experiments with the two datasets demonstrate that UnUMS can accurately estimate the proportions of different scenes for UMS.

Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches

A primary challenge in cloud detection is associated with highly mixed scenes that are filled with broken and thin clouds over inhomogeneous land. To tackle this challenge, we developed a new algorithm called the Random-Forest-based cloud mask (RFmask), which can improve the accuracy of cloud identification from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) images. For the development and validation of the algorithm, we first chose the stratified sampling method to pre-select cloudy and clear-sky pixels to form a prior-pixel database according to the land use cover around the world. Next, we select typical spectral channels and calculate spectral indices based on the spectral reflection characteristics of different land cover types using the top-of-atmosphere reflectance and brightness temperature. These are then used as inputs to the RF model for training and establishing a preliminary cloud detection model. Finally, the Super-pixels Extracted via Energy-Driven Sampling (SEEDS) segmentation approach is applied to re-process the preliminary classification results in order to obtain the final cloud detection results. The RFmask detection results are evaluated against the globally distributed United States Geological Survey (USGS) cloud-cover assessment validation products. The average overall accuracy for RFmask cloud detection reaches 93.8% (Kappa coefficient = 0.77) with an omission error of 12.0% and a commission error of 7.4%. The RFmask algorithm is able to identify broken and thin clouds over both dark and bright surfaces. The new model generally outperforms other methods that are compared here, especially over these challenging scenes. The RFmask algorithm is not only accurate but also computationally efficient. It is potentially useful for a variety of applications in using Landsat data, especially for monitoring land cover and land-use changes.

A Rigging‐Skinning Scheme to Control Fluid Simulation

AbstractInspired by skeletal animation, a novel rigging‐skinning flow control scheme is proposed to animate fluids intuitively and efficiently. The new animation pipeline creates fluid animation via two steps: fluid rigging and fluid skinning. The fluid rig is defined by a point cloud with rigid‐body movement and incompressible deformation, whose time series can be intuitively specified by a rigid body motion and a constrained free‐form deformation, respectively. The fluid skin generates plausible fluid flows by virtually fluidizing the point‐cloud fluid rig with adjustable zero‐ and first‐order flow features and at fixed computational cost. Fluid rigging allows the animator to conveniently specify the desired low‐frequency flow motion through intuitive manipulations of a point cloud, while fluid skinning truthfully and efficiently converts the motion specified on the fluid rig into plausible flows of the animation fluid, with adjustable fine‐scale effects. Besides being intuitive, the rigging‐skinning scheme for fluid animation is robust and highly efficient, avoiding completely iterative trials or time‐consuming nonlinear optimization. It is also versatile, supporting both particle‐ and grid‐ based fluid solvers. A series of examples including liquid, gas and mixed scenes are presented to demonstrate the performance of the new animation pipeline.

Risk Riding Behaviors of Urban E-Bikes: A Literature Review.

In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users’ traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.

Improved Sea Ice Fraction Characterization for L-Band Observations by the Aquarius Radiometers.

Radiometers operating at L-band (1.4 GHz) are used to retrieve sea surface salinity over ice-free oceans and have been used recently to study the cryosphere. One hindrance of their use in the high latitudes is the preponderance of mixed scenes, where seawater and sea ice are both present in the sensor's field of view (FOV). Accurately characterizing the scene is crucial for oceanographic and cryospheric applications. To that end, a sea ice fraction model, composed of passive microwave sea ice concentration retrievals and an instrument simulator that integrates radiative power coming from all around the antenna, is used. We investigate the model currently used operationally to derive the ice fraction affecting the Aquarius observations and show that it can be significantly improved. On the one hand, the current model tends to overestimate sea ice fraction in the marginal ice zone where observations are used for salinity retrievals. On the other hand, the current model underestimates ice fraction within the ice pack where observations are used to derive sea ice properties. For the northern hemisphere, we also find evidence of the sea ice type impact on L-band radiometric observations. We present a model to derive sea ice fractions that are in better agreement with Aquarius radiometric observations using the Advanced Microwave Scanning Radiometer 2 Bootstrap algorithm for sea ice concentration and using high-resolution integration over the sensor's FOV.

A Conformal Geometric Algebra Code Generator Comparison for Virtual Character Simulation in Mixed Reality

Over the last few years, recent advances in user interface and mobile computing, introduce the ability to create new experiences that enhance the way we acquire, interact and display information within the world that surrounds us with virtual characters. Virtual reality (VR) is a 3D computer simulated environment that gives to user the experience of being physically present in real or computer-generated worlds; on the other hand, augmented reality (AR) is a live direct or indirect view of a physical environment whose elements are augmented (or supplemented) by computer-generated sensory inputs. Both technologies use interactive devices to achieve the optimum adaptation of the user in the immersive world achieving enhanced presence, harnessing latest advances in computer vision, glasses or head-mounted-displays featuring embedded mobile devices. A common issue in all of them is interpolation errors while using different linear and quaternion algebraic methods when (a) tracking the user’s position and orientation (translation and rotation) using computer vision; (b) tracking using mobile sensors; (c) tracking using gesture input methods to allow the user to interactively edit the augmented scene (translation, rotation and scale) and (d) having animation blending of the virtual characters that augmented the mixed reality scenes (translation and rotation). In this work, we propose an efficient method for robust authoring (rotation) of Augmented reality scene using Euclidean geometric algebra (EGA) rotors and we propose two fast animation blending methods using GA and CGA. We also compare the efficiency of different GA code generators: (a) Gaigen library, (b) libvsr and (c) Gaalop using our animation blending methods and compare them with other alternative animation blending techniques: (a) quaternions and (b) dual-quaternions, so that a future user of GA libraries can choose the most appropriate one that will give the most optimal and faster results.

Modeling Directional Brightness Temperature Over Mixed Scenes of Continuous Crop and Road: A Case Study of the Heihe River Basin

A new geometric optical model is proposed in this letter to simulate the directional brightness temperature (DBT) distribution over mixed scenes of continuous crop and road. The DBT distributions of the crop and road zones are separately calculated, and the road zone consists of a road and adjacent crop sides. A road distribution polar map is designed to show all of the roads of different lengths, widths, and orientations in the scene. The airborne multiangle data set of the thermal infrared band that was acquired during the Heihe Watershed Allied Telemetry Experimental Research experiment is used for validation. The results demonstrate that the proposed model can simulate the DBT of a heterogeneous scene <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="TeX">$(90\times 90\ \hbox{m}^{2})$</tex-math></inline-formula> with a root-mean-square error equal to 1.1 K and good trend similarity.

Point Clouds Segmentation of Mixed Scenes with Archeological Standing Remains: A Multi-Criteria and Multi-Scale Iterative Approach

The integration of Airborne Laser Scanning (ALS) survey into archaeological research and cultural heritage management has substantially added to our knowledge of archaeological remains in forested areas, and is changing our understanding of how these landscapes functioned in the past. Further, the results of ALS-based surveys of woodlands can now potentially be incorporated into micro-regional and landscape scale studies which rely on survey data, making an important contribution to our understanding of settlement patterns and land use – but doing so requires us to recognize and manage a host of biases inherent in ALS-based survey data. While many types of archaeological remains manifest as micro-topography, several important classes of features commonly appear as standing remains. The identification of these remains is important for archaeological prospection surveys based on ALS data. Standing structures in mixed scenes with vegetation are not well addressed by standard classification approaches. In this paper we propose an approach to the identification of these structures in the point cloud based on multi-scale measures of roughness, and measures of local density and normal orientation. We demonstrate this approach using discrete-return ALS data collected in the Franche-Comté region of France.

POINT CLOUD METRICS FOR SEPARATING STANDING ARCHAEOLOGICAL REMAINS AND LOW VEGETATION IN ALS DATA

Abstract. The integration of Airborne Laser Scanning survey into archaeological research and cultural heritage management has substantially added to our knowledge of archaeological remains in forested areas, and is changing our understanding of how these landscapes functioned in the past. While many types of archaeological remains manifest as micro-topography, several important classes of features commonly appear as standing remains. The identification of these remains is important for archaeological prospection surveys based on ALS data, and typically represent structures from the Roman, Medieval and early Modern periods. Standing structures in mixed scenes with vegetation are not well addressed by standard classification approaches developed to identify bare earth (terrain), individual trees or plot characteristics, or buildings (roofed structures). In this paper we propose an approach to the identification of these structures in the point cloud based on multi-scale measures of local density, roughness, and normal orientation. We demonstrate this approach using discrete-return ALS data collected in the Franche-Comte region of France at a nominal point density of 8 pts/m2, a resolution which, in coming years, will become increasingly available to archaeologists through government supported mapping schemes.

Alleviating Moisture Effects on Remote Sensing Estimation of Crop Residue Cover

A robust model for estimating crop residue cover by remote sensing is required to better quantify management of crop residue in agricultural fields. There is a notable lack of robustness in the model, however, when the moisture contents of soil and crop residue vary. To minimize the influence of soil and crop residue moisture on spectral reflectance, we applied a preprocessing method, external parameter orthogonalization (EPO), which estimates the influence of moisture variation on reflectance by performing a principal component analysis on a small set of spectra that represent the variation in moisture. In this study, reflectance spectra (400–2400 nm) for four soils and four crop residues with different moisture conditions were acquired in the laboratory. Reflectance values of mixed scenes with varying proportions of crop residue and soil were simulated using a linear mixture model. The results indicated that moisture significantly affected the cellulose absorption index, the lignin–cellulose absorption index, and the relationships between crop residue cover and these two indices. For applying EPO to remove moisture effects, we proposed two strategies of EPO application that corresponded to two scenarios: a change in moisture in either soil or crop residue, and a change in moisture in both soil and crop residue. The results demonstrated that EPO was able to effectively remove the moisture effects and that the calibration model was more robust after EPO. This study helps to quantify and remove the effects of moisture on the estimation of crop residue cover.

Aquarius Third Stokes Parameter Measurements: Initial Results

This letter reports a first look at the polarimetric (third Stokes parameter) channel on the Aquarius L-band radiometer that was launched in June of 2011 on the Aquarius/Satélite de Aplicaciones Cientificas (SAC)-D observatory. The primary purpose of the polarimetric channel is to provide an in situ measure of Faraday rotation which can be important for remote sensing at L-band, particularly in the case of sea surface salinity. However, it also provides an additional mode of observation and a chance to look for new features of the surface. Initial results show good agreement with expectations. In particular, the values of retrieved Faraday rotation agree with predicted values, and a nonzero signal is seen to occur over mixed scenes as predicted by theory.

Priming layout of mixed scenes: Evidence of non-semantic, locally organized layout representations?

Priming layout of mixed scenes: Evidence of non-semantic, locally organized layout representations?

Assessing crop residue cover using shortwave infrared reflectance

Management of crop residues is an important consideration for reducing soil erosion and increasing soil organic carbon. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the spectral reflectance of crop residues and soils and to assess the limits of discrimination that can be expected in mixed scenes. Spectral reflectances of dry and wet crop residues plus three diverse soils were measured over the 400–2400 nm wavelength region. Reflectance values for scenes with varying proportions of crop residues and soils were simulated. Additional spectra of scenes with mixtures of crop residues, green vegetation, and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments. The spectra of dry crop residues displayed a broad absorption feature near 2100 nm, associated with cellulose-lignin, that was absent in spectra of soils. Crop residue cover was linearly related ( r 2=0.89) to the Cellulose Absorption Index (CAI), which was defined as the relative depth of this absorption feature. Green vegetation cover in the scene attenuated CAI, but was linearly related to the Normalized Difference Vegetation Index (NDVI, r 2=0.93). A novel method is proposed to assess soil tillage intensity classes using CAI and NDVI. Regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using advanced multispectral or hyperspectral imaging systems.

Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes

Quantification of plant litter cover on the soil surface is necessary in both agricultural and natural systems because the presence of litter influences the flow of nutrients, carbon, water, and energy in terrestrial ecosystems. Although remote sensing methods for measuring plant litter cover provide both a wider area of coverage and a more objective estimate of the spatial variability of litter than manual methods of quantifying the nongreen vegetation landscape components (e.g., litter or soil percent cover), it has been difficult to assess the efficiency of detecting partial litter cover over different soil types. The objectives of this study were (i) to acquire spectral reflectance data for four crop residues and two forest litter types in mixed scenes of soil and plant litter, (ii) to derive relationships that show the spectral variable, cellulose absorption index (CAI), as a function of the amount of litter on the soil surface, and (iii) to test whether the variability of soil background reflectance inhibits the detection of residues and/or the ability to quantify residue cover. Scenes of known amounts of plant litter covering three contrasting soils were prepared and their reflectance spectra (0.4–2.5 μm) were measured with a hyper-resolution spectroradiometer. Litter from four crop (corn, soybean, rice, and wheat) and two tree species (coniferous and deciduous) were included. The CAI (0.5( R 2.0+ R 2.2)− R 2.1) describes the average depth of the cellulose absorption feature at 2.1 μm in reflectance spectra. Positive values of CAI indicate the presence of cellulose. The mean CAI of the soils was −2.0 while the mean CAI of the plant litter was 5.2. CAI increased linearly for each plant litter as the amount of plant litter in the scene increased from 0% (bare soil) to 100% cover. The CAI values of mixed scenes with more than 10% litter cover were significantly larger than the CAI values of the soils. The results of this study indicate that CAI is useful for quantifying plant litter cover, even at low percent cover.

The impact of instrument field of view on measurements of cloudy-sky spectral radiances from space: application to IRIS and IMG

Spatially resolved images from the MODerate Resolution Imaging Spectrometer (MODIS) instrument are used to investigate the impact of a change in spatial field of view, from that typical of the Nimbus 4 Infrared Interferometer Spectrometer (IRIS) to that of the Interferometric Monitor for Greenhouse Gases (IMG), upon the spectral outgoing longwave radiation (OLR). Considering all-sky conditions it is found that for a typical tropical scene, approximately 150 paired measurements are required to obtain agreement to within ±2 K in the average brightness temperature ( T B), in the most transparent window channels. At mid-latitudes, the reduced scene variability means that fewer observations are required to meet the same criterion. For clear- and cloudy-sky separation a simple threshold technique based on the window T B and underlying sea-surface temperature tends to result in a systematic underestimate of the average cloudy T B by the larger field of view. A better estimate can be obtained by applying a double threshold to discriminate against the most mixed scenes.