高光谱遥感技术在水环境监测中的应用研究

以黄土高原几类常见土壤为试验品,定量研究了不同泥土含量与水体光谱反射率的关系,探索了利用反射率反演水体深度的方法。文中还介绍了利用高光谱遥感技术分析水污染类型和强度的方法。最后以靖边县城的芦河为例,分析了高光谱遥感探测水污染程度的可行性。     

Investigation of Image Fusion Between High—Resolution Image and Multi—Spectral Image

On the basis of a thorough understanding of the physical characteristics of remote sensing image,this paper employs the theories of wavelet transform and signal sampling to develop a new image fusion algorithm.The algorithm has been successfully applied to the image fusion of SPOT PAN and TM of Guangdong province, China The experimental results show that a perfect image fusion can be built up by using the image analytical solution and reconstruction in the image frequency domain based on the physical characteristics of the image formation.The method has demonstrated that the results of the image fusion do not change spectral characteristics of the original image.     

Analyzing fine-scale wetland composition using high resolution imagery and texture features

In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features).     

Next generation of global land cover characterization,mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).     

Texture augmented detection of macrophyte species using decision trees

Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 × 5 and 13 × 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier.     

Error analysis of satellite attitude determination using a vision-based approach

Improvements in communication and processing technologies have opened the doors to exploit on-board cameras to compute objects’ spatial attitude using only the visual information from sequences of remote sensed images. The strategies and the algorithmic approach used to extract such information affect the estimation accuracy of the three-axis orientation of the object.This work presents a method for analyzing the most relevant error sources, including numerical ones, possible drift effects and their influence on the overall accuracy, referring to vision-based approaches. The method in particular focuses on the analysis of the image registration algorithm, carried out through on-purpose simulations. The overall accuracy has been assessed on a challenging case study, for which accuracy represents the fundamental requirement. In particular, attitude determination has been analyzed for small satellites, by comparing theoretical findings to metric results from simulations on realistic ground-truth data. Significant laboratory experiments, using a numerical control unit, have further confirmed the outcome.We believe that our analysis approach, as well as our findings in terms of error characterization, can be useful at proof-of-concept design and planning levels, since they emphasize the main sources of error for visual based approaches employed for satellite attitude estimation. Nevertheless, the approach we present is also of general interest for all the affine applicative domains which require an accurate estimation of three-dimensional orientation parameters (i.e., robotics, airborne stabilization).     

基于定向片模型的SPOT-5遥感影像自检校光束法平差

本文通过建立内方位自检校模型,利用定向片模型对外方位元素进行内插建模,建立了基于定向片模型的自检校光束法平差,研究不同控制点数量和不同定向片间隔对定位精度的影响,并利用河南嵩山地区SPOT-5遥感影像数据对模型进行了实验验证和分析,结果表明,平面精度可以达到10.06m,高程精度可以达到8.84m.     

基于PS-InSAR的京津城际铁路地面沉降监测研究

合成孔径雷达干涉测量技术可以大范围监测地表形变,对高速铁路运行沿线具有较好的监测能力。本文以ENVISATASAR数据为基础,采用PS—InSAR监测北京和天津地区的地面沉降的特征,通过临轨基准转换完成了两个区域的拼接,实现了在同一基准上的大尺度京津城际铁路沿线地面沉降的监测。通过本次研究,我们掌握了京津城际铁路沿线地面沉降的分布,以典型区域为样方分析其时间序列演化特征,并分析城际铁路不均匀地面沉降的运行风险。     

Data fusion technique for multibeam echosoundings

On the basis of an analysis of the error sources in multibeam echosounding system, a data processing method for compensating systematic errors in multibeam survey is proposed. In order to improve the accuracy of overall swath, a data fusion technique using single beam survey data as control information for single beam and multibeam echosounding is then presented. Some questions involved in solving the adjustment problem, such as its feasibility and the numerical stability, are discussed in detail, and a two-step adjustment method is suggested. Finally, a practical survey data set is used as a case study to prove the efficiency and reliability of the proposed methods.     

Modelling stratified forest attributes using optical/LiDAR features in a central European landscape

Improvements in the acquisition of three-dimensional (3D) information from the Airborne Laser Scanner (ALS) increase its applications for studying Earth's surface. The use of ALS data in natural resource inventories is still in an experimental stage in central Europe. Here, a survey was completed in Germany, where plot-level features from LANDSAT Thematic Mapper and ALS data were applied. An automated process was developed for forest stratification using orthoimages. A genetic algorithm was applied for variable screening. Variable subsets of different sizes were employed for simultaneous predictions of structural forest attributes using the ‘Random Forest’ (RF) method. Performance was assessed by leave-one-out cross-validations on bootstrap resample data. Results indicate that the stratification of forest notably improved the results of predictions. The improvements were more obvious for the strata-related attributes. Accuracy was enhanced as the number of selected variables increased. However, parsimonious models are still essentially required for practical applications. The RF errors were slightly greater than those from least squares regression, as the non-parametric methods do not share the same mix of error components as regression. Through the combination of remote sensing and modelling, we conclude that our results are helpful for bridging the gap between regional earth observation and on-the-ground forest structure.