RapidEye卫星影像在1∶50000矿山开发遥感调查中的应用研究

在矿产资源开发多目标遥感调查与监测基础上,以红格钒钛磁铁矿—拉拉铜矿区RapidEye卫星影像为例,深入探讨了RapidEye卫星影像在1∶50 000矿山开发遥感调查中的可行性。从RapidEye图像的几何纠正精度、合成处理及遥感解译标志来看,RapidEye影像可以满足1∶50 000矿山开发遥感调查的需要,大大提高1∶50 000矿山开发遥感调查的实时性。     

遥感三维可视化技术在输电线路选线中的应用

应用遥感技术及三维可视化技术,通过遥感图像的处理、DEM生成、铁塔三维模型的建立和影像复合等工序,按照一定的比例尺和飞行路线在IMAGIS软件生成研究区的虚拟三维影像动画,为设计人员从宏观把握输电线路的路径提供了最直接的资料。     

西安城市扩展时空特征与驱动因素分析

针对西安城市扩展研究当中研究时段跨度大、政府政策对城市扩张影响分析较少等问题,该文基于1988—2015年10期Landsat影像及统计数据,利用线性趋势、多元回归分析等方法,分析了西安1988—2015年城市扩展特征及驱动因素。结果表明:西安主城区建设用地增量大、非主城区增速快,城市扩展主要发生在先后建立的经济新区当中,而第一、第二产业,城镇固定资产投资,非农业人口增加是驱动西安城市扩展的主要社会经济因素。     

任意方向对称差值核的SAR图像边缘提取算法

由于现有边缘检测方法多存在对图像噪声的敏感性以及检测边缘的有限方向性等问题,因此在斑点噪声的影响下难以有效检测SAR图像中具有任意方向性且强度相近的区域间边缘。本文提出了一种任意方向对称窗口的SAR图像边缘提取算法。首先,以任意像素在图像中的格点位置为对称中心构建任意方向上的对称窗口;定义各窗口内像素到中心像素距离的核函数;以像素核函数为权重分别计算两对称窗口内像素光谱测度的加权平均值,并定义两者之差的绝对值为该方向上该像素隶属边缘的强度;选取所有方向中最大边缘强度作为边缘像素的判别标准。为去除错误提取的边缘像素,设计基于滤波操作的后处理过程。对模拟图像提取结果的定性及定量分析表明提出算法可在一定程度上克服噪声影响,较为准确地提取任意方向的边缘。     

基于小波变换特征的遥感地貌影像纹理分析和分类

本文基于图像的正交小波变换特征，研究了遥感地貌纹理影像的特征提取和分类方法，并对２５幅地貌影像进行了分类。结果表明，所述方法不仅对同一分辨率的影像有较高的分类正确率，而且对不同分辨率的影像也有较高的分类正确率，同时对训练样本和考试样本来自不同母体的影像也有较高的分类正确率。     

考虑定向参数精度信息的TerraSAR-X和SPOT-5HRS影像RFM联合定位

卫星影像的RFM模型具有传感器无关的优点,适用于多源影像的几何定位处理,但在无地面控制点条件下联合定位时存在自主定位优势影像难以发挥主导作用且求解易发散的不足。本文通过将影像的先验自主定位精度和成像线性漂移转化为像方定向参数的精度和权信息,建立考虑影像定向参数精度信息的RFM模型。以12景TerraSAR-X和6对12景覆盖面积约为18万km2的SPOT-5HRS立体长条带影像为数据源,对两类影像定向参数先验精度配置偏差、SAR影像升降轨道方向、SAR影像数目、SAR影像分布等因素对定位精度的影响进行了系列定位试验,少量SAR与大范围HRS联合的影像自主定位平面/高程精度可达6.0m/4.2m。本文RFM平差模型无地面控制点定位精度和定向参数求解稳健性相对于传统模型有显著提升,是卫星影像无控制点1∶10万/1∶5万全球测图的一种潜在方法。     

Oasis dynamics change and its influence on landscape pattern on Jinta oasis in arid China from 1963a to 2010a: Integration of multi-source satellite images

As one of the vital research highlights of global land use and cover change, oasis change and its interaction with landscape pattern have been regarded as an important content of regional environmental change research in arid areas. Jinta oasis, a typical agricultural oasis characterized by its dramatic exploitation and use of water and land resources in Hexi corridor, northwest arid region in China, was selected as a case to study the spatiotemporal oasis change and its effects on oasis landscape pattern. Based on integration of Keyhole satellite photographs, KATE-200 photographs, Landsat MSS, TM and ETM+ images, we evaluated and analyzed the status, trend and spatial pattern change of Jinta oasis and the characteristics of landscape pattern change by a set of mathematical models and combined this information with landscape metrics and community surveys. During the period of 1963a–2010a, Jinta oasis expanded gradually with an area increase of 219.15 km², and the conversion between oasis and desert was frequent with a state of “imbalance–balance–extreme imbalance conditions”. Moreover, most of the changes took place in the ecotone between oasis and desert and the interior of oasis due to the reclamation of abandoned land, such as Yangjingziwan and Xiba townships. Furthermore, the area, size and spatial distribution of oasis were influenced by human activities and resulted in fundamental changes of oasis landscape pattern. The fractal characteristics, dispersion degree and fragmentation of Jinta oasis decreased and the oasis landscape tended to be simple and uniform. Oasis change trajectories and its landscape pattern were mainly influenced by water resource utilization, policies (especially land policies), demographic factors, technological advancements, as well as regional economic development. We found that time series analysis of multi-source remote sensing images and the application of an oasis change model provided a useful approach to monitor oasis change over a long-term period in arid area. It is recommended that the government and farmers should pay more attention to the fragility of the natural system and the government should enhance the leading role of environmental considerations in the development process of oasis change, particularly with respect to the utilization of the limited water and land resources in arid China.     

Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows

This paper is an exploratory study, which aimed to discover the synergies of data fusion and image segmentation in the context of EO-based rapid mapping workflows. Our approach pillared on the geographic object-based image analysis (GEOBIA) focusing on multiscale, internally-displaced persons’ (IDP) camp information extraction from very high spatial resolution (VHSR) images. We applied twelve pansharpening algorithms to two subsets of a GeoEye-1 image scene that was taken over a former war-induced ephemeral settlement in Sri Lanka. A multidimensional assessment was employed to benchmark pansharpening algorithms with respect to their spectral and spatial fidelity. The multiresolution segmentation (MRS) algorithm of the eCognition Developer software served as the key algorithm in the segmentation process. The first study site was used for comparing segmentation results produced from the twelve fused products at a series of scale, shape, and compactness settings of the MRS algorithm. The segmentation quality and optimum parameter settings of the MRS algorithm were estimated by using empirical discrepancy measures. Non-parametric statistical tests were used to compare the quality of image object candidates, which were derived from the twelve pansharpened products. A wall-to-wall classification was performed based on a support vector machine (SVM) classifier to classify image objects candidates of the fused images. The second site simulated a more realistic crisis information extraction scenario where the domain expertise is crucial in segmentation and classification. We compared segmentation and classification results of the original images (non-fused) and twelve fused images to understand the efficacy of data fusion. We have shown that the GEOBIA has the ability to create meaningful image objects during the segmentation process by compensating the fused image’s spectral distortions with the high-frequency information content that has been injected during fusion. Our findings further questioned the necessity of the data fusion step in rapid mapping context. Bypassing time-intensive data fusion helps to actuate EO-based rapid mapping workflows. We, however, emphasize the fact that data fusion is not limited to VHSR image data but expands over many different combinations of multi-date, multi-sensor EO-data. Thus, further research is needed to understand the synergies of data fusion and image segmentation with respect to multi-date, multi-sensor fusion scenarios and extrapolate our findings to other remote sensing application domains beyond EO-based crisis information retrieval.     

Surface reconstruction and landslide displacement measurements with Pléiades satellite images

Recent advances in image-matching techniques and VHR satellite imaging at submeter resolution theoretically offer the possibility to measure Earth surface displacements with decimetric precision. However, this possibility has yet not been explored and requirements of ground control and external topographic datasets are considered as important bottlenecks that hinder a more common application of optical image correlation for displacement measurements. This article describes an approach combining spaceborne stereo-photogrammetry, orthorectification and sub-pixel image correlation to measure the horizontal surface displacement of landslides from Pléiades satellite images. The influence of the number of ground-control points on the accuracy of the image orientation, the extracted surface models and the estimated displacement rates is quantified through comparisons with airborne laser scan and in situ global navigation satellite measurements at permanent stations. The comparison shows a maximum error of 0.13 m which is one order of magnitude more accurate than what has been previously reported with spaceborne optical images from other sensors. The obtained results indicate that the approach can be applied without significant loss in accuracy when no ground control points are available. It could, therefore, greatly facilitate displacement measurements for a broad range of applications.     

An effective thin cloud removal procedure for visible remote sensing images

Clouds are obstructions for land-surface observation, which result in the regional information being blurred or even lost. Thin clouds are transparent, and images of regions covered by thin clouds contain information about both the atmosphere and the ground. Therefore, thin cloud removal is a challenging task as the ground information is easily affected when the thin cloud removal is performed. An efficient and effective thin cloud removal method is proposed for visible remote sensing images in this paper, with the aim being to remove the thin clouds and also restore the ground information. Since thin cloud is considered as low-frequency information, the proposed method is based on the classic homomorphic filter and is executed in the frequency domain. The optimal cut-off frequency for each channel is determined semi-automatically. In order to preserve the clear pixels and ensure the high fidelity of the result, cloudy pixels are detected and handled separately. As a particular kind of low-frequency information, cloud-free water surfaces are specially treated and corrected. Since only cloudy pixels are involved in the calculation, the method is highly efficient and is suited for large remote sensing scenes. Scenes including different land-cover types were selected to validate the proposed method, and a comparison analysis with other methods was also performed. The experimental results confirm that the proposed method is effective in correcting thin cloud contaminated images while preserving the true spectral information.