沈阳市建成区扩展动态监测

以辽宁省沈阳市为研究对象,以该地区1995年、2000年、2005年以及2016年不同时相的遥感影像为信息源。首先,利用ENVI影像处理软件进行数据预处理;然后,对沈阳地区土地类型进行MLC监督分类并评定分类精度;最后,采用分类后对比的方法并通过Arc GIS软件对沈阳建成区扩展情况进行研究。     

基于Sentinel-1B SAR数据的洪水提取和监测

洪涝灾害是我国最严重的气象灾害之一,及时准确的洪灾监测是防灾减灾的重要前期工作和基础。本文利用sentinel-1B雷达数据,以黑瞎子岛为研究区,联合使用OSTU阈值分割法和随机森林面向对象分类法针对像素统计单波形、双波形、多波形SAR影像提取洪水要素,实现对洪灾淹没面积的时序监测,为灾情监测提供数据和技术支撑。     

基于改进BP神经网络的海底底质分类

通过采用遗传算法优化神经网络初始权值的方法,将GA算法与BP神经网络有机结合,应用于海底底质分类。基于多波束测深系统获取的反向散射强度数据,应用改进的BP神经网络分类方法,实现对海底基岩、砾石、砂、细砂和泥等底质类型的快速、准确识别。通过实验比较,GA-BP神经网络分类精度明显高于BP神经网络,证明了该方法的有效性和可靠性。     

水产养殖遥感监测及信息自动提取方法研究

以海南省文昌地区为例,在沿海地区尝试利用遥感监测的手段提取水产养殖地块和面积.采用面向对象的分类方法进行监测,取得了理想效果,同时也提出了提高分类精度的建议.     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

改进的P-SVM支持向量机与遥感数据分类

本文介绍了将P-SVM算法引入多光谱/高分辨率遥感数据的分类, 并且展示了卫星ASTER和航空ADS40数字影像分类的技术过程和结果验证。结果表明：P-SVM方法的分类精度不低于SVM, 并减少了时耗。     

Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery

The airborne laser scanning LIDAR (LIght Detection And Ranging) provides high-resolution Digital Terrain Models (DTM) that have been applied recently to the characterization, quantification and monitoring of coastal environments. This study assesses the contribution of LIDAR altimetry and intensity data, topographically-derived features (slope and aspect), and multi-spectral imagery (three visible and a near-infrared band), to map coastal habitats in the Bidasoa estuary and its adjacent coastal area (Basque Country, northern Spain). The performance of high-resolution data sources was individually and jointly tested, with the maximum likelihood algorithm classifier in a rocky shore and a wetland zone; thus, including some of the most extended Cantabrian Sea littoral habitats, within the Bay of Biscay. The results show that reliability of coastal habitat classification was more enhanced with LIDAR-based DTM, compared with the other data sources: slope, aspect, intensity or near-infrared band. The addition of the DTM, to the three visible bands, produced gains of between 10% and 27% in the agreement measures, between the mapped and validation data (i.e. mean producer's and user's accuracy) for the two test sites. Raw LIDAR intensity images are only of limited value here, since they appeared heterogeneous and speckled. However, the enhanced Lee smoothing filter, applied to the LIDAR intensity, improved the overall accuracy measurements of the habitat classification, especially in the wetland zone; here, there were gains up to 7.9% in mean producer's and 11.6% in mean user's accuracy. This suggests that LIDAR can be useful for habitat mapping, when few data sources are available. The synergy between the LIDAR data, with multi-spectral bands, produced high accurate classifications (mean producer's accuracy: 92% for the 16 rocky habitats and 88% for the 11 wetland habitats). Fusion of the data enabled discrimination of intertidal communities, such as Corallina elongata, barnacles (Chthamalus spp.), and stands of Spartina alterniflora and Phragmites australis, which presented misclassification when conventional visible bands were used alone. All of these results were corroborated by the kappa coefficient of agreement. The high classification accuracy found here, selecting data sources, highlights the value of integrating LIDAR data with multi-spectral imagery for habitat mapping in the intertidal complex fringe.     

Mapping benthic habitat in regions of gradational substrata: An automated approach utilising geophysical,geological, and biological relationships

The relationship between acoustic backscatter, sediment characteristics and benthic habitat is examined using high-resolution sidescan sonar data collected at the Loch Linnhe artificial reef site on the west coast of Scotland. The site is typical for the continental shelf of NW Europe, with a mix of seabed environments from muddy to coarse, stony substrata on a 10–100 m length scale. A sidescan sonar mosaic was produced and classified according to derived backscatter parameters (mean, median and standard deviation of the backscatter values) using an unsupervised classification procedure. The accuracy of the final classified map was assessed by comparison with a ground-truthing survey in which the biological habitat was derived from underwater video footage. The sidescan correctly predicted seabed surface characteristics of observed biological habitat with 78% accuracy. A second, and more challenging test of the acoustic data to correctly predict biological habitat was made by comparing it with data from 21 grab sampling stations. These stations were divided into three groups using multivariate statistical techniques based on their backscatter properties. Benthic assemblage structure was found to be significantly distinct between the high and low, and the medium and low backscatter stations. There was a low to moderate but significant correlation between the multivariate patterns of acoustic backscatter, benthic assemblage structure, and particle size distribution. The work shows that even in areas with subtle and gradational changes in substratum, the sidescan was able to predict biological community with an acceptable accuracy.     

海洋工程污染分类研究

给出海洋工程的概念和定义,对海洋工程污染的分类和研究现状进行了讨论。从海洋工程污染的工程特征要素、污染特征要素、程度要素和管理要素进行了分类研究,认为海洋工程污染的分类,可按其工程特征要素,对其进行离海岸的远近、离海底的高度、使用材质、移动与否等分类;可按其工程污染要素,对其进行化学污染、物理污染、生物损害、地质损害与污染等分类;可按其工程污染影响的深度特征要素,对其进行重度影响、中度影响和轻度影响分类;可按其工程污染的管理角度,对其进行海洋工程和海岸工程的分类。     

对《土地利用现状分类》中有关问题的探讨

按照土地的属性、利用方式和覆盖特征,结合土地利用特点,对土地利用现状分类含义进行了深入的研究,认为部分土地分类的含义值得商榷,并提出了解决问题的途径.