InSAR相位干涉图滤波方法研究

合成孔径雷达干涉测量(InSAR)中相位干涉图的质量是决定干涉测量精度的关键因素之一。在其他影响因素确定的情况下,干涉图滤波是提高干涉图质量的有效手段。介绍多视数均值滤波与基于梯度的自适应滤波,分析其优缺点,在此基础上,探讨这两种滤波相结合的综合滤波方案,提出直接对InSAR相位干涉图的实部和虚部进行综合滤波的方法,给出具体流程图。最后,通过对实际SAR数据的处理验证所提出的滤波方法抑噪效果明显,且能很好地保持相位干涉图的边缘信息。

星载ScanSAR工作模式研究与设计

该文系统地阐述了星载ScanSAR系统的基本工作原理,由ScanSAR系统的空间几何模型,确定了ScanSAR系统各子条带的空间位置,研究了ScanSAR系统的方位分辨率问题,确定了ScanSAR系统工作的时间关系,各子条带的脉冲重复频率的选择及每个burst的样本数,为系统设计提供了依据。重点研究了 ScanSAR系统的信噪比及距离模糊问题,提出了一种新的 ScanSAR系统特有的模糊概念ARASR,并提出新的分析方法,对其影响做了定量的计算,最后对 ScanSAR系统的数据下载格式提出了建议。

Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening(Ts HARP) method and a binary linear model are compared to downscale the original daytime FengY un 2 F(FY-2 F) land surface temperature(LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land(SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error(RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data.

A multi-view K-multiple-means clustering method

The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data sets.And aiming at the problem that it cannot be applied to the Internet on a multi-view data set,a multi-view K-multiple-means(MKMM)clustering method is proposed in this paper.The new algorithm introduces view weight parameter,reserves the design of setting multiple subclasses,makes the number of clusters as constraint and obtains clusters by solving optimization problem.The new algorithm is compared with some popular multi-view clustering algorithms.The effectiveness of the new algorithm is proved through the analysis of the experimental results.

Prediction of Soil Depth from Digital Terrain Data by Integrating Statistical and Visual Approaches

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models （DEMs） with geographic information systems （GIS） and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the A1-Muwaqqar Watershed （70 km2） in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.

A parallel memory architecture for video coding

To efficiently exploit the performance of single instruction multiple data （SIMD） architectures for video coding, a parallel memory architecture with power-of-two memory modules is proposed. It employs two novel skewing schemes to provide conflict-free access to adjacent elements （8-bit and 16-bit data types） or with power-of-two intervals in both horizontal and vertical directions, which were not possible in previous parallel memory architectures. Area consumptions and delay estimations are given respectively with 4, 8 and 16 memory modules. Under a 0.18-pm CMOS technology, the synthesis results show that the proposed system can achieve 230 MHz clock frequency with 16 memory modules at the cost of 19k gates when read and write latencies are 3 and 2 clock cycles, respectively. We implement the proposed parallel memory architecture on a video signal processor （VSP）. The results show that VSP enhanced with the proposed architecture achieves 1.28× speedups for H.264 real-time decoding.

Statistical Multiplexing of Video Traffic and Data Traffic

This paper studies statistical multiplexing performance by input of video traffic and data traffic. The inputs have different Qos requirements such as loss and delay jitter. By applying a modified FBM model, we present methods to estimate effective bandwidth of the aggregated traffic. Simulations were performed to evaluate effective bandwidth. The comparison between the estimation and the simulation shows that the estimations can give correct data for the effective bandwidths in terms of our interests. The analysis of gain by using priority multiplexing also addresses proper Qos configuration for the inputs in order to achieve positive gains.

Characteristics of Digital Broadcasting Technology and Its Development Research

Driven by the rapid development of agricultural science and technology, many techniques have been developed rapidly, especially the development of broadcasting technology which earth-shaking changes have taken place. The advent of digital broadcasting technology makes the broadcast technology enter a new stage. With its low energy consumption and large coverage area, digital broadcasting technology was widely welcomed by the broadcast television industry. This paper analyzes digital audio broadcasting, multimedia digital broadcasting and digital AM broadcasting, and further concretely elaborates the application prospects of digital broadcasting technology.

View-invariant human action recognition via robust locally adaptive multi-view learning

Human action recognition is currently one of the most active research areas in computer vision. It has been widely used in many applications, such as intelligent surveillance, perceptual interface, and content-based video retrieval. However, some extrinsic factors are barriers for the development of action recognition; e.g., human actions may be observed from arbitrary camera viewpoints in realistic scene. Thus, view-invariant analysis becomes important for action recognition algorithms, and a number of researchers have paid much attention to this issue. In this paper, we present a multi-view learning approach to recognize human actions from different views. As most existing multi-view learning algorithms often suffer from the problem of lacking data adaptiveness in the nearest neighborhood graph construction procedure, a robust locally adaptive multi-view learning algorithm based on learning multiple local L 1-graphs is proposed. Moreover, an efficient iterative optimization method is proposed to solve the proposed objective function. Experiments on three public view-invariant action recognition datasets, i.e., ViHASi, IXMAS, and WVU, demonstrate data adaptiveness, effectiveness, and efficiency of our algorithm. More importantly, when the feature dimension is correctly selected （i.e., 〉60）, the proposed algorithm stably outperforms state-of-the-art counterparts and obtains about 6% improvement in recognition accuracy on the three datasets.

Geo-Scape,a Granularity Depended Spatialization Tool for Visualizing Multidimensional Data Sets

Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity.The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods.Furthermore,this paper introduces the prototyping tool Geo-Scape,which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity,by making use of a kernel density estimation technique and on the landscape "smoothness" metaphor.A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data,by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity.