Effects of channel morphology and sensor spatial resolution on image-derived depth estimates

The utility of remote sensing in the study of fluvial systems depends upon the extent to which image data can be used to derive quantitative information of sufficient accuracy and precision for specific applications. In this study, we evaluate the effects of channel morphology on depth retrieval by coupling a radiative transfer model to various morphologic scenarios. Upwelling radiance L_u spectra generated for a range of depths (2-150 cm) and benthic cover types (limestone, periphyton, and gravel) were linearly mixed to simulate sub-pixel bed topography and substrate heterogeneity. For sloping bottoms, solar-streambed geometry modified L_u relative to a level bottom, especially for beds sloping steeply away from the sun. Aggregate pixel scale L_u spectra were compared to a database of simulated spectra to determine the radiance-equivalent depth of a uniform bottom (REDUB). REDUB spectra for hypothetical stepped streambeds indicated underestimation of the actual area-weighted mean depth, but the ln (L_u,560/L_u,690) REDUB ratio consistently reproduced the pixel-scale mean for beta distributions of depths. Similarly, when both dark periphyton and bright limestone substrates occurred within a pixel, REDUB spectra produced large errors while the ratio proved robust. Along channel banks, pixels will inevitably be mixed, and our simulations indicated that although bank fractions estimated by spectral mixture analysis were highly accurate for vegetated cutbanks, gravel bars were sensitive to the selection of both aquatic and terrestrial end members and subject to relatively large fraction errors. These theoretical results were tested using a ratio-based relative depth map and two-end member mixture models derived from a hyperspectral image of the Lamar River in Yellowstone National Park, which also served to illustrate the importance and applicability of our simulations. The primary conclusions of this study are that 1) the ratio-based algorithm is well-suited to complex river channels; 2) channel morphology and sensor spatial resolution must be considered jointly during data collection and analysis; and 3) the accuracy and precision of depth estimates are influenced by channel morphology and thus vary spatially.     

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钻井中，需要对将要钻达的下部地层深度进行预测，特别是在定向井的钻探过程中显得尤为重要。目前的井斜角、方位能不能满足钻达目标靶区的地质需要，或者地下地质情况（地层厚度、倾向、倾角）发生变化后钻井措施的相应改变，以及需要调整改变的钻井参数怎样确定等，都需要经过严密的计算和分析。文章对钻井轨迹和地质情况进行了分析，从地质层位的小层入手，对构造的走向、倾向、倾角，地层的真厚、铅直厚度，钻井的方位、井斜角、实钻地层厚度等参数进行了分析，在三维空间的基础上，建立了实钻的数学模型，明确了各参数间的关系。推导出的公式适用于包括定向井在内的钻井参数计算，可以在地层钻厚预测、深度对比，钻达目标靶区的水平位移及井斜角、方位角的调整，穿越储层厚度的计算及钻井参数的调整，目标靶区是否在气水界面以内及钻达预想海拔高程的钻井参数调整等多个方面进行运用，这对下步钻井达到地质目的具有重要的指导作用。     

CFR为主体的缓速酸研究及现场应用

研制了多效酸液添加剂ＣＦＲ，以ＣＦＲ为主体配制成功了具有良好缓蚀，缓速，稳定三价铁离子和低表面张力等性能的缓速酸液体系。报导了ＣＦＲ及其酸液的配方和各项性能，介绍了在陕甘宁中部气田２口井上用ＣＦＲ缓速酸成功地进行酸化压裂的实例。     

矩形和T形截面受弯构件承载力计算

本文根据砼结构设计规范（ＧＢＪ１０－８９）给出了常见矩形，Ｔ形、工字形截面受弯构在Ａｓ‘处于弹性状态或屈服时，所需受拉钢筋Ａｓ的直接求解设计方程，这些方程可用于手算也可用于电算。     

SIMS对氘靶钛膜中氘的分布研究

用负的ＳＩＭＳ分析技术进行Ｈ同位素相对含量Ｔｉ膜深度变化的分析，获得了氘随膜深度变化的分布曲线。在接近膜表面的一个深度范围内，除有明显的Ｏ＾－、ＣＯ＾－和很微弱的ＯＨ＾－，ＯＤ＾－及ＣＨＯ＾－峰谱外，还有很大的Ｄ＾－二次离子质谱峰，氘的丰度估计约为９８％。在靶膜内部，氘的丰度达９９％以上。     

三峡建库对上荆江航道影响的略估

在简述三峡水库调度运用基本图形及其所引起的水沙条件变化基础上,通过略估认为,三峡建库后因枯季泄量增大及河床纵向冲刷下切,上荆江沙质河段的航道水深将会明显增加。     

复杂难选矿石深度还原技术应用研究现状

本文介绍了处理复杂难选矿石的新工艺——深度还原技术,并归纳总结了深度还原技术在处理鲕状赤铁矿、白云鄂博氧化矿和红土镍矿几种公认的复杂难选矿石中的应用研究现状,指出了该工艺的发展方向。通过与其他方法的对比分析,得出深度还原技术在对我国复杂难选矿石的开发利用过程中优越性较为明显,为处理复杂难选矿石开辟了一个新的途径。     

基于整体和个体分割融合的双人交互行为识别

在人类交互行为识别领域,基于RGB视频的局部特征往往不能有效区分近似动作,将深度图像（Depth）与彩色图像（RGB）在识别过程中进行融合,提出一种融合Depth信息的整体和个体分割融合的双人交互行为识别算法。该算法首先分别对RGB和Depth视频进行兴趣点提取,在RGB视频上采用3DSIFT进行特征描述,在Depth视频上利用YOLO网络对左右两人兴趣点进行划分,并使用视觉共生矩阵对局部关联信息进行描述。最后使用最近邻分类器分别对RGB特征和Depth特征进行分类识别,进一步通过决策级融合两者识别结果,提高识别准确率。结果表明,结合深度视觉共生矩阵可以大大提高双人交互行为识别准确率,对于SBU Kinect interaction数据库中的动作可以达90%的正确识别率,验证了所提算法的有效性。     

Depth video spatial and temporal correlation enhancement algorithm based on just noticeable rendering distortion model

Spatial and temporal inconsistency of depth video deteriorates encoding efficiency in three dimensional video systems. A depth video processing algorithm based on human perception is presented. Firstly, a just noticeable rendering distortion (JNRD) model is formulated by combining the analyses of the influence of depth distortion on virtual view rendering with human visual perception characteristics. Then, depth video is processed based on the JNRD model from two aspects, spatial and temporal correlation enhancement. During the process of spatial correlation enhancement, depth video is segmented into edge, foreground, and background regions, and smoothened by Gaussian and mean filters. The operations of the temporal correlation enhancement include temporal–spatial transpose (TST), temporal smoothing filter and inverse TST. Finally, encoding and virtual view rendering experiments are conducted to evaluate the proposed algorithm. Experimental results show that the proposed algorithm can greatly reduce the bit rate while it maintains the quality of virtual view.