REST API技术及在移动通信网络管理中的应用

介绍了目前最炙手可热的REST架构风格，该风格顺应Web2.0的兴起，完美的匹配了云计算时代来临的可扩展要求，在各种应用场景中都得到了充分的表现。根据其技术特点，分析了该风格的API在移动通信网络管理中的应用，从网管系统内部、网管系统之间以及网管系统与上层APP应用之间等多方面对是否适用于REST风格以及如何在合适的位置使用REST API进行了分析。     

利用TM资料测量毛乌素沙地面积的方法和结果

利用1:25万陆地卫星TM资料和地形图，在GIS支持下，建立毛乌素沙地图形数据库，对毛乌素沙地目前流沙分布的范围进行了调查研究。重.资、介绍了利用陆地卫星TM资料对毛乌素沙地的面积进行测童计算的技术和方法，给出了最新测贡得到的毛乌素沙地全区总面积和分布于各旗县的沙地面积数据。     

TM1300多媒体DSP的百兆以太网通信接口的设计实现

主要研究TM1300多媒体数字信号处理器(DSP)在pSOS嵌入式操作系统下的100 Mbit/s高速以太网接口的设计与实现.硬件方面,介绍了TM1300系统与RTL8139以太网驱动芯片通过PCI总线互连的实现方式,重点介绍了其中PCI仲裁器的设计和PCI配置空间地址的设置方法;软件方面,介绍了在DSP端内嵌的pSOS操作系统下开发软件驱动RTL8139芯片的方法,重点描述了pSOS的pNA 网络组件的NI接口、RTL8139芯片收发数据的控制方式、驱动程序的发送缓存管理、TM1300访问PCI空间的途径以及内存与数据缓存一致性等要点.最后从速率、可靠性、效率3个方面对实现的以太网通信接口进行了测试,给出了详细的实测结果.测试结果显示,与基于RTL8029芯片开发的以太网接口相比,各项性能均有较大提高.     

IRS-P6 LISS-3和Landsat-5 TM两种多光谱传感器影像数据的对比分析

随着载有各种新型传感器的卫星相继发射升空,不同传感器之间的相互比较成了一个研究热点。首先从传感器的轨道特征、光谱范围等方面对IRS-P6 LISS-3和Landsat-5 TM进行了机理方面的对比分析,选取3对同日过空的遥感影像,研究了IRS-P6 LISS-3和Landsat-5 TM遥感数据在可见光-近红外、短红外各对应光谱波段之间的关系,建立TM和LISS-3各波段之间的相互转换公式,与Chander等提出的转换公式进行对比分析。结果表明:实际TM和模拟TM多光谱数据之间具有较强的相关关系,决定系数R²均大于0.97;模拟TM与实际TM的水体指数(MNDWI)和归一化植被指数(NDVI)空间散点分布和实际LISS-3与实际TM的空间散点分布相比,具有更强的相关关系,其决定系数R²有一定提高,散点分布趋于对称。因此,所求的关系转换方程具有较高的精度和有效性,效果优于Chander等提出的转换公式。     

分段线性动态矩匹配条带去除

由于探测器之间对接收的地物辐射信号的响应特征不同,导致遥感数据含有条带噪声,严重影响了图像质量及后续的定量计算。针对探测器响应函数在图像低值区及高值区呈非线性的特点,在着重分析矩匹配方法的基础上,提出分段线性动态矩匹配条带去除方法。方法设定阈值分割高中低值域统计区间,对探测器响应函数进行分段线性拟合,并对探测器每一分图像动态采用其领域内均值和标准差作为参考值进行条带纠正。应用TM数据第4波段及环境一号卫星高光谱数据进行去条带实验,并定性和定量地比较了该方法与动态矩匹配、傅里叶变换、自动均衡化曲线方法的去条带效果。结果表明该方法能够在保留图像基本信息的前提下,获得最佳的去条带效果,尤其能够提高非均匀地物分布区域内水体的条带去除效果。     

漓江流域近15年遥感植被覆盖度f_(veg)变化特征

为研究漓江流域近15年植被覆盖度变化情况,以1991、2000、2006年3个时相的TM/ETM+影像为基础,采用基于归一化植被指数(NDVI)的像元二分模型对漓江流域植被覆盖度进行估算,分析了植被覆盖度时空变化特征;结合研究区地质数据,进一步分析地质条件对植被覆盖度的影响。结果表明:①受气候和人类活动的影响,1991—2000年漓江流域植被覆盖度有所增加,表现为高和较高植被覆盖区面积增加,中度、较低和低植被覆盖区面积减少;2000—2006年漓江流域植被覆盖度略有降低,表现为高植被覆盖区面积减少,较高和中度植被覆盖区增加,较低和低植被覆盖区面积减少;②由于地质条件的影响,非岩溶区的平均植被覆盖度高于岩溶区,两者的变化趋势与流域整体变化基本一致;③从空间分布上看,1991—2006年漓江流域植被覆盖度的变化较显著。     

复杂地形下自动提取多暗像元的TM影像大气纠正方法

复杂地形条件下气溶胶的空间分布变化较大,用单一的气溶胶实测参数或单一的暗像元进行大气纠正都难以获得好的效果,手动选取暗像元还受限于专业人员的经验。通过程序自动提取图像中的浓密植被像元以及位于山区阴影的植被像元作为暗像元,使得暗像元均匀分布于图像的各个区域、各个海拔,更好地模拟复杂地形下的大气状况。根据辐射传输模型,利用迭代、插值等方法获取TM1和TM3波段光学厚度,进而推算成像时刻的气溶胶波长指数及混浊度系数,从而得到各个波段的大气光学厚度以实现大气纠正。     

Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)

Understanding, monitoring and modelling attributes of seagrass biodiversity, such as species composition, richness, abundance, spatial patterns, and disturbance dynamics, requires spatial information. This work assessed the accuracy of commonly available airborne hyper-spectral and satellite multi-spectral image data sets for mapping seagrass species composition, horizontal horizontal-projected foliage cover and above-ground dry-weight biomass. The work was carried out on the Eastern Banks in Moreton Bay, Australia, an area of shallow and clear coastal waters, containing a range of seagrass species, cover and biomass levels. Two types of satellite image data were used: Quickbird-2 multi-spectral and Landsat-5 Thematic Mapper multi-spectral. Airborne hyper-spectral image data were acquired from a CASI-2 sensor using a pixel size of 4.0 m. The mapping was constrained to depths shallower than 3.0 m, based on past modelling of the separability of seagrass reflectance signatures at increasing water depths. Our results demonstrated that mapping of seagrass cover, species and biomass to high accuracy levels (> 80%) was not possible across all image types. For each parameter mapped, airborne hyper-spectral data produced the highest overall accuracies (46%), followed by Quickbird-2 and then Landsat-5 Thematic Mapper. The low accuracy levels were attributed to the mapping methods and difficulties in matching locations on image and field data sets. Accurate mapping of seagrass cover, species composition and biomass, using simple approaches, requires further work using high-spatial resolution (< 5 m) and/or hyper-spectral image data. Further work is required to determine if and how the seagrass maps produced in this work are suitable for measuring attributes of seagrass biodiversity, and using these data for modelling floral and fauna biodiversity properties of seagrass environments, and for scaling-up seagrass ecosystem models.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

Atmospheric correction of optical imagery from MODIS and Reanalysis atmospheric products

In this paper we analyze the differences obtained in the atmospheric correction of optical imagery covering bands located in the Visible and Near Infra-Red (VNIR), Short-Wave Infra-Red (SWIR) and Themal-Infrared (TIR) spectral regions when atmospheric profiles extracted from different sources are used. In particular, three sensors were used, Compact High Resolution Imaging Spectrometer (CHRIS), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) and Landsat5 Thematic Mapper (TM), whereas four atmospheric profiles sources were considered: i) local soundings launched near the sensor overpass time, ii) Moderate Resolution Radiometer (MODIS) atmospheric profiles product (MOD07), iii) Atmospheric Correction Parameter Calculator (ACPC) generated by the National Center for Environmental Prediction (NCEP) and iv) Modified Atmospheric Profiles from Reanalysis Information (MAPRI), which includes data from NCEP and National Center of Atmospheric Research (NCAR) Reanalysis project but interpolated to 34 atmospheric levels and resampled to 0.5° × 0.5°. MODIS aerosol product (MOD04) was also used to extract Aerosol Optical Thickness (AOT) values at 550 nm. Analysis was performed for three test dates (12th July 2003, 18th July 2004 and 13th July 2005) over an agricultural area in Spain. Results showed that air temperature vertical profiles were similar for the four sources, whereas dew point temperature profiles showed significant differences at some particular levels. Atmospheric profiles were used as input to MODTRAN4 radiative transfer code in order to compute atmospheric parameters involved in atmospheric correction, with the aim of retrieving surface reflectances in the case of VNIR and SWIR regions, and Land Surface Temperature (LST) in the case of the TIR region. For the VNIR and SWIR region, significant differences depending on the atmospheric profile used were not found, particularly in the Visible region in which the AOT content is the main parameter involved in the atmospheric correction. In the case of TIR, differences depending on the atmospheric profile used were appreciable, since in this case the main parameter involved in the atmospheric correction is the water vapor content, which depends on the vertical profile. In terms of LST retrieval from ASTER data (2004 test case), all profiles provided satisfactory results compared to the ones obtained when using a local sounding, with errors of 0.3 K for ACPC and MAPRI cases and 0.7 K for MOD07. When retrieving LST from TM data (2005 test case), errors for MOD07 and MAPRI were 0.6 and 0.9 K respectively, whereas ACPC provided an error of 2 K. The results presented in this paper show that the different atmospheric profile sources are useful for accurate atmospheric correction when local soundings are not available. In particular, MOD07 product provides atmospheric information at the highest spatial resolution, 5 km, although its use is limited from 2000 to present, whereas MAPRI provides historical information from 1970 to present, but at lower spatial resolution.