A Generative Model of Natural Texture Surrogates

Natural images can be viewed as patchworks of different textures, where the local image statistics is roughly stationary within a small neighborhood but otherwise varies from region to region. In order to model this variability, we first applied the parametric texture algorithm of Portilla and Simoncelli to image patches of 64X64 pixels in a large database of natural images such that each image patch is then described by 655 texture parameters which specify certain statistics, such as variances and covariances of wavelet coefficients or coefficient magnitudes within that patch. To model the statistics of these texture parameters, we then developed suitable nonlinear transformations of the parameters that allowed us to fit their joint statistics with a multivariate Gaussian distribution. We find that the first 200 principal components contain more than 99% of the variance and are sufficient to generate textures that are perceptually extremely close to those generated with all 655 components. We demonstrate the usefulness of the model in several ways: (1) We sample ensembles of texture patches that can be directly compared to samples of patches from the natural image database and can to a high degree reproduce their perceptual appearance. (2) We further developed an image compression algorithm which generates surprisingly accurate images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how our approach can be used for an efficient and objective evaluation of samples generated with probabilistic models of natural images.Comment: 34 pages, 9 figure

Surface Engineering for Phase Change Heat Transfer: A Review

Among numerous challenges to meet the rising global energy demand in a sustainable manner, improving phase change heat transfer has been at the forefront of engineering research for decades. The high heat transfer rates associated with phase change heat transfer are essential to energy and industry applications; but phase change is also inherently associated with poor thermodynamic efficiencies at low heat flux, and violent instabilities at high heat flux. Engineers have tried since the 1930's to fabricate solid surfaces that improve phase change heat transfer. The development of micro and nanotechnologies has made feasible the high-resolution control of surface texture and chemistry over length scales ranging from molecular levels to centimeters. This paper reviews the fabrication techniques available for metallic and silicon-based surfaces, considering sintered and polymeric coatings. The influence of such surfaces in multiphase processes of high practical interest, e.g., boiling, condensation, freezing, and the associated physical phenomena are reviewed. The case is made that while engineers are in principle able to manufacture surfaces with optimum nucleation or thermofluid transport characteristics, more theoretical and experimental efforts are needed to guide the design and cost-effective fabrication of surfaces that not only satisfy the existing technological needs, but also catalyze new discoveries

Phenology-based UAV remote sensing for classifying invasive annual grasses to the species level

The spread of invasive plant species severely alters wildfire regimes, degrades critical habitat for native species, and has detrimental impacts on ecosystem function, rangeland productivity, and long-term carbon storage dynamics. Remote sensing technology has greatly improved our understanding of invasive plant ecology and ability to map and monitor plant invasions. Mapping plant invasions to the species level with conventional satellite and airborne data has proven challenging, however, because many invasive species occur at fine spatial scales or are mixed with native species, and satellite passes may occur too infrequently to capture important phenological stages. Imagery derived from readily deployable Unmanned Aerial Vehicles (UAVs) offers high-resolution data over carefully timed acquisition dates during the growing season. However, some challenges remain that are particular to high spatial resolution imagery, where excessive detail from shadows and canopy gaps often result in misclassification, inaccuracy, and a “salt-and-pepper” effect in the final classification. The addition of textural and vegetation height data to a purely spectral pixel-based approach has the potential to mitigate these challenges and improve species-level vegetation classification. Using UAV imagery acquired at specific phenological stages, we investigate which combinations of spectral, textural, vegetation height, and multi-temporal techniques best separate two invasive annual grasses, cheatgrass and medusahead, to the species level.We selected five study sites ranging in area from 8 to 36 hectares (ha) in Paradise Valley, Nevada, which feature a variety of invasive and native species that are typical of the Great Basin region. For three carefully selected dates over the growing season during which cheatgrass and medusahead were most spectrally distinct, we conducted UAV flight campaigns and collected field data on vegetation composition. Imagery was processed in photogrammetric software to produce orthomosaics, digital terrain models, and digital surface models from which vegetation height was derived. Texture analysis was performed over the acquired raster data products. Multi-date spectral, textural, and vegetation height variables were used to predict vegetation class type using Random Forest machine learning methods. The overall goal of this research is to further remote sensing methods for vegetation classification of invaded landscapes to the species level. We investigated which combinations of spectral, textural, vegetation height, and multitemporal techniques best separate two invasive annual grasses - cheatgrass and medusahead. To explore the impact of explanatory variables in our classification, all possible additive combinations of our variables were calculated. We found that multi-temporal texture variables and vegetation height added additional levels of information to our classification and, when combined with multi date spectral information, achieved the highest overall accuracy. Our model resulted in a robust classification across several diverse study sites

An Empirical Bayesian Approach to Quantify Multi-Scale Spatial Structural Diversity in Remote Sensing Data

Landscape structure is as much a driver as a product of environmental and biological interactions and it manifests as scale-specific, but also as multi-scale patterns. Multi-scale structure affects processes on smaller and larger scales and its detection requires information from different scales to be combined. Herein, we propose a novel method to quantify multi-scale spatial structural diversity in continuous remote sensing data. We combined information from different extents with an empirical Bayesian model and we applied a new entropy metric and a value co-occurrence approach to capture heterogeneity. We tested this method on Normalized Difference Vegetation Index data in northern Eurasia and on simulated data and we also tested the effect of coarser pixel resolution. We find that multi-scale structural diversity can reveal itself as patches and linear landscape features, which persist or become apparent across spatial scales. Multi-scale line features reveal the transition zones between spatial regimes and multi-scale patches reveal those areas within transition zones where values are most different from each other. Additionally, spatial regimes themselves can be distinguished. We also find the choice of scale need not be informed by typical length-scales, which makes the method easy to implement. The proposed multi-scale approach can be applied to other contexts, following the roadmap we pave out in this study and using the tools available in the accompanying R package StrucDiv

Detection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure

We propose lacunarity as a novel recurrence quantification measure and illustrate its efficacy to detect dynamical regime transitions which are exhibited by many complex real-world systems. We carry out a recurrence plot-based analysis for different paradigmatic systems and nonlinear empirical data in order to demonstrate the ability of our method to detect dynamical transitions ranging across different temporal scales. It succeeds to distinguish states of varying dynamical complexity in the presence of noise and non-stationarity, even when the time series is of short length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and geometric features beyond linear structures in the recurrence plot can be accounted for. This makes lacunarity more broadly applicable as a recurrence quantification measure. Lacunarity is usually interpreted as a measure of heterogeneity or translational invariance of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogeneity in the temporal recurrence patterns at all relevant time scales. We demonstrate the potential of the proposed method when applied to empirical data, namely time series of acoustic pressure fluctuations from a turbulent combustor. Recurrence lacunarity captures both the rich variability in dynamical complexity of acoustic pressure fluctuations and shifting time scales encoded in the recurrence plots. Furthermore, it contributes to a better distinction between stable operation and near blowout states of combustors