Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

Bibliographic data

Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WA-1 KNOWLEDGE-BASED TECHNIQUES/ SYSTEMS FOR DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Information Fusion in Cartographic Feature Extraction from Aerial Imagery. David M. McKeown, Frederic P. Perlant, Jefferey Shufelt

Document type:: Multivolume work

Structure type:: Chapter

142 Recent work has addressed the problem of fusing the information provided by analysis systems such as BABE, SHADE, SHAVE, and GROUPER into more complete and accurate sets of building hypotheses. A monocular fusion system has been developed that merges the geometric building boundaries into composite boundaries. Figure 4 shows the fusion result for the Washington, D.C. scene. Performance analysis indicates that the percentage of building pixels identified correctly in this scene for SHADE, SHAVE and GROUPER is 37.5%, 47.2%, and 48.7%, respectively. The results of monocular fusion, however, improve the overall building pixel classification rate to 77.7%. We have performed fusion analysis over a test database of 8 images and have observed similar performance improvements 10, . 2.1 Further Issues in Monocular Fusion The problems of building structure hypothesis integration are amenable to the techniques of information fusion. Recent work in this area has shown that simple image processing techniques can integrate complementary sources of building hypothesis data to provide improved detection of man-made objects in aerial imagery 11 . These building fusion methods provide a simple and effective means for increasing the building detection rate for a scene. There remain, however, other issues to be addressed in the fusion of monocular scene analysis systems. The fusion techniques produce qualitatively accurate building delineations, in the sense that few buildings are overlooked in the extraction process; quantitative delineations, however, are typically poor due to the accumulation of delineation errors in the data produced by the monocular analysis subsystems. While applications such as flight simulation are not necessarily adversely affected by this problem, cartographic applications may require very accurate delineations of man-made structures. We would like to address this issue by examining the interactions between data during the information fusion process. For example, during the fusion process, an estimate of "building density" can be produced; that is, an estimate of the likelihood of building structure for each pixel in an image. These likelihood estimates could be used to refine the fused building delineations. In addition, the fusion process provides a composite set of building boundaries. Various subsets of these boundaries could be combined and evaluated with respect to image gradient, shadow casting, and disparity measures to produce improved boundaries. Another approach would use these boundaries as intermediate results to be analyzed by each of the component monocular analysis systems. These systems could refine their initial building estimates in accordance with the fused boundaries, and then these refined estimates could themselves be integrated to produce improved fusions. This idea suggests an iterative process in which building fusions are used to refine their components until some quality measure is exceeded. If multiple views of a scene are available, either from different vantage points, or from sensors with different spectral characteristics, then more information is available for an information fusion process. Under the cooperative- methods paradigm, monocular analysis techniques could be applied to multiple images to produce initial building detection data, which could then be integrated by information fusion techniques to produce improved building detection rates for the scene. Although multiple sets of data may not always be available, their presence requires the development and/or extension of fusion techniques to take advantage of the additional information such data provides. 3 Improvement and Interpretation of 3D Scene Analysis The main goal of many vision systems, and in particular automated cartographic feature extraction systems, is to recover the three-dimensional structure of a scene. Stereo analysis is a common technique used for this problem, and much work has been done in this area. Nevertheless, the difficulty of the task (in terms of registration and stereo matching) limits the success of any single technique. To obtain complete and accurate height estimates for a scene, it seems clear that we will need to utilize more knowledge than that used by any stereo matching process. Some work is being done on the interpolation of sparse disparity information to generate reliable and dense height information, and work is being done on post-processing of stereo matching results to ensure consistency of the derived height estimates. Many three-dimensional estimation improvement approaches use external knowledge about the scene, in the form of models that constrain certain aspects of scene structure. An interesting approach would he the use of information from multiple sources to achieve consistent disparity results, by applying a simple information fusion model. Such an approach allows for the integration of incomplete and inconsistent information to refine height estimates. Initial work under this paradigm used a region-based interpretation model for the information fusion and refinement process 7 . The assumption of this model is that uniform image radiometry is produced by planar surfaces, of specific orientations and materials. Under this assumption, the segmentation of the monocular images into fine surface patches of nearly homogeneous intensity will ideally result in a segmentation delineating planar surfaces in the scene. Having obtained segmentations of the images in which regions of nearly homogeneous intensity are delineated, it becomes possible to refine initial height estimates for the scene. Since each region is assumed to correspond to a planar surface in the scene, we can assign disparity values to each region to produce an initial refinement of the height estimates. This is done by histogramming the disparity values of each region and selecting the most representative value for the region. Figure 5 shows a smoothed image of an industrial area in Washington, D.C. Figure 6 shows a segmentation obtained by a recursive histogram splitting technique 12 , and Figure 7 shows a refined S2 disparity map 13 . Experiments have shown that such region-based representations of a scene provide useful models for the refinement of height estimates. Using the fusion approach, it becomes possible to segment the scene into building regions based solely on disparity information. (Figure 8 shows the building regions extracted by such an approach). Other sources of information could be utilized at the refinement stage to further enhance disparity data. Left/right consistency constraints could augment the fusion model, as well as more sophisticated models of image radiometry and surface structure. The region-based refinement approach could also be used to refine scene segmentations (such as those produced by feature extraction systems). In summary, the information fusion and refinement paradigm provides a framework for the enhancement of height estimates and allows for the incorporation of possibly inaccurate or inconsistent information in a robust manner.

Access restriction

Copyright

fullscreen: Proceedings of the Symposium on Global and Environmental Monitoring (Part 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation