WorldView卫星影像的稀少控制数字空三生产研究

根据WorldView高分辨率卫星影像的特点,结合数字空中三角测量原理,为了减少外业像控点工作量,进行稀少控制多模型区域网平差方法进行卫星影像的数字空中三角测量生产试验。在数字空三完成后,进行模型拼接检查,使用一定数量的检查点检查立体模型成果精度,最终得出对卫星影像使用稀少控制多模型区域网平差方法可达到的精度指标。最终为WorldView卫星影像的稀少控制数字空三作业提供可靠的理论依据和合理的作业方法。     

Developing spatially and thematically detailed backdated maps for land cover studies

ABSTRACT

Global or regional land cover change on a decadal time scale can be studied at a high level of detail using the availability of remote sensing data such as that provided by Landsat. However, there are three main technical challenges in this goal. First, the generation of land cover maps without reference data is problematic (backdating). Second, it is important to maintain high accuracies in land cover change map products, requiring a reasonably rich legend within each map. Third, a high level of automation is necessary to aid the management of large volumes of data. This paper describes a robust methodology for processing time series of satellite data over large spatial areas. The methodology includes a retrospective analysis used for the generation of training and test data for historical periods lacking reference information. This methodology was developed in the context of research on global change in the Iberian Peninsula. In this study we selected two scenes covering geographic regions that are representative of the Iberian Peninsula. For each scene, we present the results of two classifications (1985–1989 and 2000–2004 quinquennia), each with a legend of 13 categories. An overall accuracy of over 92% was obtained for all 4 maps.     

Adaptation of the SNOWMAP algorithm for snow mapping over eastern Canada using Landsat-TM imagery

Abstract

The snow mapping algorithm SNOWMAP was adapted to Landsat-TM data and to the context of eastern Canada. Six Landsat-5 TM scenes were used. It was found that the original version of SNOWMAP greatly underestimates snow cover extent. The modification made to the original algorithm, by cancelling the minimum threshold of 0.1 on the NDVI value, allows gaps to be filled in. In addition, a spatial correction procedure applied to the modified SNOWMAP algorithm results improves snow detection under coniferous forests. Based on a limited data set of ground-based observations (only 40 sites were available), the modified SNOWMAP seems to perform better in snow detection than the original version of the algorithm. An application case is presented in order to demonstrate the relevance of the modified SNOWMAP results as a high spatial-resolution reference for the validation of historical snow maps derived from medium spatial-resolution satellite data.

Citation Chokmani, K., Dever, K., Bernier, M., Gauthier, Y. & Paquet, L.-M. (2010) Adaptation of the SNOWMAP algorithm for snow mapping over eastern Canada using Landsat-TM imagery. Hydrol. Sci. J. 55(4), 649–660.     

Forest cover trends from time series Landsat data for the Australian continent

In perennial and natural vegetation systems, monitoring changes in vegetation over time is of fundamental interest for identifying and quantifying impacts of management and natural processes. Subtle changes in vegetation cover can be identified by calculating the trends of a vegetation density index over time. In this paper, we apply such an index-trends approach, which has been developed and applied to time series Landsat imagery in rangeland and woodland environments, to continental-scale monitoring of disturbances within forested regions of Australia. This paper describes the operational methods used for the generation of National Forest Trend (NFT) information, which is a time-series summary providing visual indication of within-forest vegetation changes (disturbance and recovery) over time at 25 m resolution. This result is based on a national archive of calibrated Landsat TM/ETM+ data from 1989 to 2006 produced for Australia's National Carbon Accounting System (NCAS). The NCAS was designed in 1999 initially to provide consistent fine-scale classifications for monitoring forest cover extent and changes (i.e. land use change) over the Australian continent using time series Landsat imagery. NFT information identifies more subtle changes within forested areas and provides a capacity to identify processes affecting forests which are of primary interest to ecologists and land managers. The NFT product relies on the identification of an appropriate Landsat-based vegetation cover index (defined as a linear combination of spectral image bands) that is sensitive to changes in forest density. The time series of index values at a location, derived from calibrated imagery, represents a consistent surrogate to track density changes. To produce the trends summary information, statistical summaries of the index response over time (such as slope and quadratic curvature) are calculated. These calculated index responses of woody vegetation cover are then displayed as maps where the different colours indicate the approximate timing, direction (decline or increase), magnitude and spatial extent of the changes in vegetation cover. These trend images provide a self-contained and easily interpretable summary of vegetation change at scales that are relevant for natural resource management (NRM) and environmental reporting.     

Object extraction based on evolutionary morphological processing

This paper introduces a novel technique for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structure element, which is derived by genetic algorithms. The population of morphological filtersis iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring element using the evolution principles of genetic search. Experimental results of road extraction from high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.     

改进SIFT小型无人机视频序列图像自动拼接方法

针对小型无人机视频序列图像视点离散、视角变化有一定运动规律的特点,本文结合Harris特征点和SIFT特征向量的优势,研究实现图像的自动配准;并采用光度对准的方法和加权平均的方法消除光度差异,较好地实现了视频序列图像的无缝拼接。实验证明该方法在图像拼接的准确性、效率等方面较经典的SIFT算法有较大提高。     

Several strategies on 3D modeling of manmade objects

Several different strategies of 3D modeling are adopted for different kinds of manmade objects. Firstly, for those mammade objects with regular structure, if 2D information is available and elevation information can be obtained conveniently, then 3D modeling of them can be executed directly. Secondly, for those mammade objects with complicated structure comparatively and related stereo images pair can be acquired, in the light of topology-based 3D model we finish 3D modeling of them by integrating automatic and semi-automatic object extraction. Thirdly, for the most complicated objects whose geometrical information cannot be got from stereo images pair completely, we turn to topological 3D model based on CAD.     

Change detection from aerial images acquired in different durations

Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on old and new aerial images acquired in different durations. The corresponding theory and experiments are introduced and analyzed in this paper. The main procedure includes four stages. The new and old images have to be registered firstly. Then image matching, based on the maximum correlation coefficient, is performed between registered images after the low contrast areas have been removed. The regions with low matching quality are extracted as candidate changed areas. Thirdly, the Gaussian-Laplacian operator is used to detect edges in candidate changed areas on both the registered images, and the straight lines are detected by Hough transformation. Finally, the changed houses and roads can be detected on the basis of straight line matching in candidate changed areas between registered images. Some experimental results show that the method introduced in this paper is effective.     

Integrated Use of Optical and Radar Satellite Data for Active Faults and Corresponding Displaced Landforms in Kozani Basin (Greece)

Abstract

The usefulness of integration of SAR (ERS-1) and Landsat TM data for study active faults and the corresponding displaced landforms in flat or almost flat areas has been demonstrated. The study area is the Kozani basin which in May 13, 1995 was affected by a strong earthquake (Ms=6.6). After co-registration and resampling the two data sets were merged to form a combined image. The combined image offers the spectral characteristics of the TM data with the spatial resolution and roughness sensitivity of SAR images. The merging method used was the IMS to RGB transform. The criteria and parameters examined were geomorphic features, drainage network analysis, slope processes, terrain analysis, and observations on spatial distribution of soil cover as well as linear features that correspond to fracture zones crossing the basin. The use of the combined image allowed us to identify tectonic terraces in the basin produced by activity of normal faults located in the adjacent relief zone.     

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products,using Landsat data and C-CAP products

This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.