SPOT stereo matching for Digital Terrain Model generation (original) (raw)
Related papers
1992
This paper presents a matching algorithm for automatic DTM generation from SPOT images that provides dense, accurate and reliable results and attacks the problem of radiometric differences between the images. The proposed algorithm is based on a modified version of the Multiphoto Geometrically Constrained Matching (MPGC). It is the first algorithm that explicitly uses the SPOT geometry in matching, restricting thus the search space in one dimension, and simultaneously providing pixel and object coordinates. This leads to an increase in reliability, and to reduction and easier detection of blunders. The sensor modelling is based on Kratky's polynomial mapping functions to transform between the image spaces of stereopairs. With their help epipolar lines that are practically straight can be determined and the search is constrained along these lines. The polynomial functions can also provide approximate values, which are further refined by the use of an image pyramid. Radiometric differences are strongly reduced by performing matching not in the grey level but in gradient magnitude images. Thus, practically only the information in stripes along the edges is used for matching. Edges that exist in only one image can be detected by subtracting quasi registered images in the upper levels of an image pyramid and by consistency checks. The points to be matched are selected by an interest operator in preprocessed gradient images. Gross errors can be detected by statistical analysis of criteria that are provided by the algorithm. The results of an extensive test using a stereo SPOT model over Switzerland will be reported. Different cases of radiometric differences, and matching with different options and the qualitative comparison of the results based on forty thousand check points will be presented.
Image and Vision Computing, 2003
This paper addresses the problem of generating digital elevation models from satellite images taken by linear pushbroom cameras. Since there exist unique geometric properties for linear pushbroom images, we argue that the conventional DEM generation schemes developed for perspective images are not suitable for satellite images. Using the geometric properties of linear pushbroom images, we design a new matching strategy optimized for linear pushbroom image in three aspects: conjugate search method, correlation patch design and match sequence determination. We will discuss in what aspect conventional approaches and our new approach differ and show how performance has improved by hiring proper techniques. A series of experiments using SPOT panchromatic stereo pairs showed that our approach outperformed conventional approaches in terms of accuracy and processing time.
Determination of terrain models by digital image matching methods
2004
ABSTRACT: Today, digital terrain models (DTMs) are used in many fields of science and practice. When modelling the earth's surface it is necessary to make a clear distinction between terrain models, ie models representing the terrain in the sense of the 'bare soil', ...
2016
This paper presents a matching algorithm for automatic DTM generation from SPOT images that provides dense, accurate and reliable results and attacks the problem of radiometric differences between the images. The proposed algorithm is based on a modified version of the Multiphoto Geometrically Constrained Matching (MPGC). It is the first algorithm that explicitly uses the SPOT geometry in matching, restricting thus the search space in one dimension, and simultaneously providing pixel and ob-ject coordinates. This leads to an increase in reliability, and to reduction and easier detection of blunders. The sensor modelling is based on Kratky’s polynomial mapping functions to transform between the image spaces of stereopairs. With their help epipo-lar lines that are practically straight can be determined and the search is constrained along these lines. The polynomial functions can also provide approximate values, which are further refined by the use of an image pyramid. Radiometric diff...
An approach for automatic stereo model generation using non-metric digital images
The evolution of non-metric digital cameras and its integration with direct orientation sensors (GPS/INS), make feasible some applications that require fast mapping, like thematic and cadastral mapping, disasters management and environment monitoring. However, the accuracies of the GPS/INS sensors are usually not enough to generate a stereo-model without vertical parallax, hindering the stereoscopic visualization of the scene and affecting the 3D reconstruction. This paper presents an automatic methodology for removing the vertical parallax in the model, based on a modified coplanarity model. Some tie points are automatically measured in the model using area-based correspondence methods, integrating methods to reduce the search space and an a priori analysis of the matching areas to increase the robustness of the process. After the EO parameters adjustment, the images are normalized through an epipolar resampling, in order to provide a suitable stereoscopic visualization and to facilitate the process of automatic Digital Terrain Model (DTM) generation. A methodology for DTM generation, based on the adjusted EO parameters and epipolar images is presented as well. Some experiments in a test area were performed, and the results obtained are discussed, showing the effectiveness of the proposed approach.
SPOT stereo matching for DTM generation
Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, 1993
This paper presents a matching algorithm for automatic DTM generation from SPOT images that provides dense, accurate and reliable results and attacks the problem of radiometric differences between the images. The proposed algorithm is based on a modified version of the Multiphoto Geometrically Constrained Matching (MPGC). It is the first algorithm that explicitly uses the SPOT geometry in matching, restricting thus the search space in one dimension, and simultaneously providing pixel and object coordinates. This leads to an increase in reliability, and to reduction and easier detection of blunders. The sensor modelling is based on Kratky's polynomial mapping functions to transform between the image spaces of stereopairs. With their help epipolar lines that are practically straight can be determined and the search is constrained along these lines. The polynomial functions can also provide approximate values, which are further refined by the use of an image pyramid. Radiometric differences are strongly reduced by performing matching not in the grey level but in gradient magnitude images. Thus, practically only the information in stripes along the edges is used for matching. Edges that exist in only one image can be detected by subtracting quasi registered images in the upper levels of an image pyramid. The points to be matched are selected by an interest operator. Gross errors can be detected by statistical analysis of criteria that are provided by the algorithm and by a robust analysis of the heights within local neighbourhoods. The results of an extensive test using a stereo SPOT model over Switzerland will be reported. Matching with different options and the qualitative comparison of the results based on thirty thousand check points will be presented.
High Accuracy 3D Processing of Stereo Satellite Images in Mountainous Areas
Mountain areas are very crucial places for the future monitoring and management of precious natural resources of mankind. Among the various monitoring tools aerial and satellite images will play an ever increasing role. In particular highresolution satellite images at sub-5m footprint with stereo capabilities are in the focus of interest. They are becoming increasingly available through more and more missions, better performance and at seemingly reduced costs. They constitute an excellent device for accurate and fast information extraction over large areas, even in very inaccessible terrain. The related cameras are all using Linear Array CCD technology for image sensing. The possibility and need for accurate 3D object reconstruction and georeferencing requires sophisticated camera and trajectory models, being able to deal with such sensor geometry. We have recently developed a full suite of functions and the related software for the precision processing of this kind of data. The corresponding software SAT-PP (Satellite Imagery Precision Processing) includes a number of functionalities and module which will briefly be addressed in this paper. The software can accommodate images from IKONOS, Quickbird, ALOS PRISM, SPOT-5, Cartosat-1 and sensors of similar type to be expected in the future. The functionality of the methods and software will be verified by results from validation projects in Switzerland (testfield Bern/Thun) and Italy (testfield Piemont). We put particular emphasis on the georeferencing aspects, the automatic generation of DSMs, which can be done with pixel level accuracy.
Accuracy and Completeness of Topographic Mapping from Spot Imagery
The Photogrammetric Record, 2006
A previous paper described the geometric model used to find the exterior orientation of dynamic SPOT imagery. The present paper considers the results of measurement and interpretation tests performed on a number of stereomodels. The image quality is assessed in comparison with the original digital data. Level 1A stereomodels with diflerent base:height ratios, numbers of control points and with different control point accuracies are compared. Accuracies obtained with level 1P and 1B data are also reported. The information content of the imagery is assessed by feature plotting followed by comparison with the 1:SO 000 and 1:lOO 000 scale maps of the area. The importance of high quality photographic imagery and operator experience are noted.