Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

Mapping spiny aster infestations with QuickBird imagery

QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas, USA rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false colour composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer's and user's accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer's and user's accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.     

无棱镜技术在电厂施工土方量测量中的应用

随着全站仪在测量中的广泛应用,测量的精度有了很大的提高.特别是无棱镜技术的发展,改变了以往的测量手段和方法,提高了工作的效率.土方量测量是施工过程中不可避免的,为此本文介绍无棱镜测量技术在洛河电厂施工过程中测量土方量的应用情况,通过对比实验,我们对无棱镜测量技术工作效率和成果精度以及适用条件进行分析.     

单基站CORS系统在中小区域测绘中的应用探讨

本文对单基站CORS系统的建设和应用进行了探讨,通过单基站CORS系统在成都市龙泉驿区的建设使用情况和数据检验分析,表明单基站CORS系统完全可以胜任中小区域中的一般测量工作。其作业的有效范围广,在保证测量精度的前提下,提高了作业效率,降低了劳动强度和生产成本。     

Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.     

浙江省海宁市TM图像土地利用自动分类精度评价方法的试验研究

按照国家土管局土地利用现状分类标准,利用叠合的海宁市长安镇１∶１万同时期土地详查电子地图的全域地面实况数据,在遥感图像处理常规分类方法的范畴内,采取最大程度利于提高分类精度的措施,所得的分类精度上限为６５％。研究表明,通常在试验区的小块局部中选择训练样本和选择评价分类精度的参考像元,对精度评价有显著影响,参考像元质量和数量的限制很可能是这些年来一些同类研究报道了较高分类精度结果的主要原因     

Effects of laser beam alignment tolerance on lidar accuracy

One of the major lidar error sources not yet analyzed in the literature is the tolerance of the laser beam alignment with respect to the scanning mirror. In this paper, the problem of quantifying these errors is solved for rotating polygon mirror type lidar systems. An arbitrary deviation of the beam from its design direction–the vector of beam misalignment–can be described by two independent parameters. We choose these as horizontal and vertical components of the misalignment vector in the body frame. Either component affects both, horizontal and vertical lidar accuracy. Horizontal lidar errors appear as scan line distortions—along and across track shifts, rotations and scaling. It is shown that the horizontal component of misalignment results in a scan line first being shifted across the track and then rotated around the vertical at the new center of the scan line. Resulting vertical lidar error, being a linear function of the scan angle, is similar to that produced by a roll bias. The vertical component of the beam misalignment causes scan line scaling and an along track shift. The corresponding vertical error is quadratic with respect to the scan angle. The magnitude of these effects is significant even at tight alignment tolerances and cannot be realistically accounted for in the conventional calibration model, which includes only range, attitude and GPS biases. Therefore, in order to attain better accuracy, this model must be expanded to include the beam misalignment parameters as well. Addition of new parameters into the model raises a question of whether they can be reliably solved for. To give a positive answer to this question, a calibration method must utilize not only ground control information, which is typically very limited, but also the relative accuracy information from the overlapping flight lines.     

A Review of Catalogues of Chinese Earthquakes

In the past decade, the most authoritative catalogues of Chinese earthquakes and the most popular with seismologists in China are the following:(1) Gu Gongxu, 1983, Catalogue of Chinese Earthquakes;(2) Min Ziqun, 1988, Concise Catalogue of Chinese Earthquakes;(3) Xie Yusuou, 1989, Catalogue of Chinese Earthquakes (M≥ 4.7) from 1900-1980 with Uniform Magnitudes; and(4) Min Ziqun, 1995, Catalogue of Chinese Historical Strong Earthquakes. Earthquakes that occurred before 1900 are mainly documented in historical records.Since 1950s, more recent earthquakes were documented in two major compilations of historical records finished in 1956 and 1983-1987. Separately this effort resulted in two chronicles: two volumes for the first one and five volumes for the second one. The magnitudes are converted from the maximum intensity. These magnitudes, by convention, are connected with surface wave magnitudes. However, it is clear that they do not have any strict seismological definition.The period of 1900-1962 documen     

速度差对井间走时层析成像质量的影响及其改进方法

在井间地震初至走时层析成像中,随着相邻地质体速度差的增大,使得射线分布不均匀,以及网格剖分不合适,导致层析成像结果不理想.物理和数值模型的井间走时层析成像表明：当速度差超过30.0%时,层析结果畸变较大;在30.0%～15.0%之间时,层析结果较好;低于15.0%时,层析结果好.在此基础上,提出了井间多尺度初至走时层析成像方法,即对同一模型采用多种网格剖分来同时进行层析成像,以获得研究区域的速度图像.数学和物理模型的井间多尺度走时层析结果表明：该方法很好地兼顾了层析成像的分辨率和精度,极大地改善了井间地震层析成像的质量.即使速度差超过30%,其多尺度的层析结果仍然较好.因此,这种方法具有实际应用的潜力.     

2008年T213与德国降水数值预报产品对比检验

为了解各种数值预报的误差特点,更好地在预报过程中选择数值预报产品作为参考依据,将中国国家气象中心的T213降水预报与德国降水预报分别进行晴雨预报检验,对2008年5—8月东北地区降水资料进行对比分析。结果表明：两种模式24-120h预报正确率为60％-70％,随着预报时效的增加,正确率呈下降趋势,德国降水预报的正确率高于T213,两种预报漏报率均明显小于空报率,T213漏报率较低,为5％左右,德国降水预报空报率较低,为20％左右。对2008年4-6月出现东北冷涡过程的两种模式降水预报进行对比分析,发现德国降水预报正确率明显高于T213预报,对冷涡降水预报有一定的指示意义。