Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

基于COSM的三维显微图像复原算法研究

光学分层显微成像技术(COSM)不仅可用于去除非聚焦层杂散光的影响以及噪声的干扰,而且能复原样本的清晰三维像,将成为提取微流体的三维温度和速度场分布信息的有效工具.本文研究了基于已知点扩展函数的6种COSM成像算法,详细讨论了6种COSM成像算法的原理和实现,构造了三维样本图像和点扩展函数模型,并对复原结果进行了分析....     

局部方差在图像质量评价中的应用

将灰度图像的局部方差分布（QLS）作为表征图像结构信息的一个重要特征,对局部方差分布矩阵进行奇异值分解,计算得到相应的奇异值特征向量;通过计算降质图像与原参考图像局部方差矩阵奇异值特征向量的夹角大小度量两图像的结构相似度,实现了对降质图像的质量评价。实验结果表明：局部方差分布更能突出图像的结构特征,评价结果优于传统的均方误差（MSE）、峰值信噪比（PSNR）、结构相似度（SSIM）以及直接评价图像像素分布的奇异值分析（SVD）等方法,与人眼视觉感知效果的一致性较好。     

Optical extensometer and elimination of the effect of out-of-plane motions

A novel optical extensometer is developed to estimate the local uniform strain on planar surface accurately. The proposed system consists of a shared large format lens and two image sensors, which acquire pairs of images of two isolated small regions on the object surface simultaneously. Digital image correlation (DIC) algorithm is applied to determine the relative displacement between gauge points designated on recorded pairs of images. Then local strain can be extracted after dividing the relative displacement by the scale distance. Moreover, a special experimental setup called “correction sheet” is used to eliminate the virtual strain induced by out-of-plane motions. Uni-axial tensile experiments are performed to validate the reliability and resolution of the optical extensometer, and the measurement results demonstrate that the resolution of the optical extensometer achieves 2–3 με.     

Gaussian pre-filtering for uncertainty minimization in digital image correlation using numerically-designed speckle patterns

This paper discusses the effect of pre-processing image blurring on the uncertainty of two-dimensional digital image correlation (DIC) measurements for the specific case of numerically-designed speckle patterns having particles with well-defined and consistent shape, size and spacing. Such patterns are more suitable for large measurement surfaces on large-scale specimens than traditional spray-painted random patterns without well-defined particles. The methodology consists of numerical simulations where Gaussian digital filters with varying standard deviation are applied to a reference speckle pattern. To simplify the pattern application process for large areas and increase contrast to reduce measurement uncertainty, the speckle shape, mean size and on-center spacing were selected to be representative of numerically-designed patterns that can be applied on large surfaces through different techniques (e.g., spray-painting through stencils). Such “designer patterns” are characterized by well-defined regions of non-zero frequency content and non-zero peaks, and are fundamentally different from typical spray-painted patterns whose frequency content exhibits near-zero peaks. The effect of blurring filters is examined for constant, linear, quadratic and cubic displacement fields. Maximum strains between ±250 and ±20,000 µε are simulated, thus covering a relevant range for structural materials subjected to service and ultimate stresses. The robustness of the simulation procedure is verified experimentally using a physical speckle pattern subjected to constant displacements. The stability of the relation between standard deviation of the Gaussian filter and measurement uncertainty is assessed for linear displacement fields at varying image noise levels, subset size, and frequency content of the speckle pattern. It is shown that bias error as well as measurement uncertainty are minimized through Gaussian pre-filtering. This finding does not apply to typical spray-painted patterns without well-defined particles, for which image blurring is only beneficial in reducing bias errors.     

Mass spectrometry imaging as a tool for surgical decision‐making

Despite significant advances in image‐guided therapy, surgeons are still too often left with uncertainty when deciding to remove tissue. This binary decision between removing and leaving tissue during surgery implies that the surgeon should be able to distinguish tumor from healthy tissue. In neurosurgery, current image‐guidance approaches such as magnetic resonance imaging (MRI) combined with neuronavigation offer a map as to where the tumor should be, but the only definitive method to characterize the tissue at stake is histopathology. Although extremely valuable information is derived from this gold standard approach, it is limited to very few samples during surgery and is not practically used for the delineation of tumor margins. The development and implementation of faster, comprehensive, and complementary approaches for tissue characterization are required to support surgical decision‐making – an incremental and iterative process with tumor removed in multiple and often minute biopsies. The development of atmospheric pressure ionization sources makes it possible to analyze tissue specimens with little to no sample preparation. Here, we highlight the value of desorption electrospray ionization as one of many available approaches for the analysis of surgical tissue. Twelve surgical samples resected from a patient during surgery were analyzed and diagnosed as glioblastoma tumor or necrotic tissue by standard histopathology, and mass spectrometry results were further correlated to histopathology for critical validation of the approach. The use of a robust statistical approach reiterated results from the qualitative detection of potential biomarkers of these tissue types. The correlation of the mass spectrometry and histopathology results to MRI brings significant insight into tumor presentation that could not only serve to guide tumor resection, but that is also worthy of more detailed studies on our understanding of tumor presentation on MRI. Copyright © 2013 John Wiley & Sons, Ltd.     

Scanning Probe Microscopy beyond Imaging: A General Tool for Quantitative Analysis

A simple, fast and general approach for quantitative analysis of scanning probe microscopy (SPM) images is reported. As a proof of concept it is used to determine with a high degree of precision the value of observables such as 1) the height, 2) the flowing current and 3) the corresponding surface potential (SP) of flat nanostructures such as gold electrodes, organic semiconductor architectures and graphenic sheets. Despite histogram analysis, or frequency count (Fc), being the most common mathematical tool used to analyse SPM images, the analytical approach is still lacking. By using the mathematical relationship between Fc and the collected data, the proposed method allows quantitative information on observable values close to the noise level to be gained. For instance, the thickness of nanostructures deposited on very rough substrates can be quantified, and this makes it possible to distinguish the contribution of an adsorbed nanostructure from that of the underlying substrate. Being non‐numerical, this versatile analytical approach is a useful and general tool for quantitative analysis of the Fc that enables all signals acquired and recorded by an SPM data array to be studied with high precision.