In this paper we address the registration of close range imagery to virtual urban models, using buildings and other fixed objects in a scene. We introduce a novel approach, using radiometric and spatial queries to support the registration of ground level imagery. Image registration involves the comparison of an image’s content to the information contained in a VR model, to identify in the VR model the facades that best resemble the ones contained in the processed imagery. This registration-through-queries approach allows us to use coarse information in the form of imprecisely outlined facades to perform image registration, removing the requirements for time-consuming processes like precise delineation of control point measurement. In the paper we introduce radiometric indexing schemes to support object facade queries, and present experiments to demonstrate the function of these metrics in our image registration framework.
The acquisition and update of Geographic Information System (GIS) data are typically carried out using aerial or satellite imagery. Since new roads are usually linked to georeferenced pre-existing road network, the extraction of pre-existing road segments may provide good hypotheses for the updating process. This paper addresses the problem of extracting georeferenced roads from images and formulating hypotheses for the presence of new road segments. Our approach proceeds in three steps. First, salient points are identified and measured along roads from a map or GIS database by an operate or an automatic tool. These salient points are then projected onto the image-space and errors inherent in this process are calculated. In the second step, the georeferenced roads are extracted from the image using a dynamic programming algorithm. The projected salient points and corresponding error estimates are used as input for this extraction process. Finally, the road center axes extracted in the previous step are analyzed to identify potential new segments attached to the extracted, pre-existing one. This analysis is performed using a combination of edge-based and correlation-based algorithms. In this paper we present our approach and early implementation results.
Handling change within integrated geospatial environments is a challenge of dual nature. It comprises automatic change detection, and the fundamental issue of modeling/representing change. In this paper we present a novel approach for automated change detection which allows us to handle change more efficiently than commonly available approaches. More specifically, we focus on the detection of building boundary changes within a spatiotemporal GIS environment. We have developed a novel approach, as an extension of least-squares based matching. Previous spatial states of an object are compared to its current representation in a digital image, and decisions are automatically made as to whether or not change at the outline has occurred. Older object information is used to produce templates for comparison with the representation of the same object in a newer image. Semantic information extracted through an analysis of template edge geometry, and estimates of accuracy are used to enhance our model. This template matching approach allows us to integrate in a single operation object extraction from digital imagery with change detection. By decomposing a complete outline into smaller elements and applying template matching along these locations we are able to detect precisely even small changes in building outlines. In this paper we present an overview of our approach, theoretical models, certain implementation issues like template selection and weight coefficient assignment, and experimental results.
This paper deals with monoscopic object extraction from digital imagery by least squares template matching. We present a globally enforced least squares template matching method, constrained by internal shape forces, for automatic precise geometric identification and registration of object outlines. This is a highly localized operation, and as such, it can be affected by gaps in the edge representation, occlusions, radiometric noise, and other artifacts. To bypass this problem, the method is extended to an object-wise global level by being performed simultaneously for the complete outline of a single object. The template matching least squares solutions for different edge segments along the outline of an object are tied together by using a geometric coherence condition which expresses the a priori acceptable shape behavior of this object. Finally, we discuss the use of the presented technique within a semi-automated monoplotting strategy for GIS object extraction.
Conference Committee Involvement (6)
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII
15 September 2008 | Cardiff, Wales, United Kingdom
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology
17 September 2007 | Florence, Italy
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI
13 September 2006 | Stockholm, Sweden
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V
19 September 2005 | Bruges, Belgium
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV
14 September 2004 | Maspalomas, Canary Islands, Spain
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III
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