Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

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

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:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 3 OBJECT AND IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INCLUSION OF MULTISPECTRAL DATA INTO OBJECT RECOGNITION. Bea Csathó , Toni Schenk, Dong-Cheon Lee and Sagi Filin

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 53 INCLUSION OF MULTISPECTRAL DATA INTO OBJECT RECOGNITION 1 2 Bea Csathó , Toni Schenk, Dong-Cheon Lee and Sagi Filin 1 Byrd Polar Research Center, OSU, 1090 Carmack Rd., Columbus, OH 43210, email: csatho.l@osu.edu, phone: 1-614-292-6641 2 Department of Civil Engineering, OSU, 2070 Neil Ave., Columbus, OH 43210 email: schenk.2@osu.edu, phone: 1-614-292-7126 KEYWORDS: Data fusion, multisensor, classification, urban mapping, surface reconstruction. ABSTRACT In this paper, we describe how object recognition benefits from exploiting multispectral and multisensor datasets. After a brief introduction we summarize the most important principles of object recognition and multisensor fusion. This serves as the basis for the proposed architecture of a multisensor object recognition system. It is characterized by multistage fusion, where the different sensory input data are processed individually and only merged at appropriate levels. The remaining sections describe the major fusion processes. Rather than providing detailed descriptions, a few examples, obtained from the Ocean City test-data site, have been chosen to illustrate the processing of the major data streams. The test site comprises of multispectral and aerial imagery, and laser scanning data. 1. INTRODUCTION The ultimate goal of digital photogrammetry is the automation of map making. This entails understanding aerial imagery and recognizing objects - both hard problems. Despite of the increased research activities and the remarkable progress that has been achieved, systems are still far from being operational and the far-reaching goal of an automatic map making system remains a dream. Before an object, e.g. a building, can be measured, it must first be identified as such. Fully automated systems have been developed for recognizing certain objects, such as buildings and roads on monocular aerial imageries, but their performance largely depends on the complexity of the scene and other factors (Shufelt, 1999). However, the utilization of multiple sensory input data, or other ancillary data, such as DEMs or GIS layers, opens new avenues to approach the problem. By combining sensors that use different physical principles and record different properties of the object space, complementary and redundant information becomes available. If merged properly, multisensor data may lead to a more stable and consistent scene description. Active research topics in object recognition include multi-image techniques using 3D feature extraction, DEM analysis or range images from laser scanning, map- or GIS-based extraction, color or multispectral analysis, and/or a combination of these techniques. Now the cardinal question is how to exploit the potential these different data sources offer to tackle object recognition more effectively. Ideally, proven concepts and methods in remote sensing, digital photogrammetry and computer vision should be combined in a synergistic fashion. The combination may be possible through the use of multisensor data fusion, or distributed sensing. Data fusion is concerned with the problem of how to combine data from multiple sensors to perform inferences that may not be possible from a single sensor alone (Hall, 1992). In this paper, we propose a unified framework for object recognition and multisensor data fusion. We start out with a brief description of the object recognition paradigm, followed by the discussion of different architectures for data fusion. We then propose a multisensor object recognition system. The remaining sections describe the major fusion processes. Rather than providing detailed descriptions, a few examples, obtained from the Ocean City test-data site, have been chosen to illustrate the processing of the major data streams. Csatho and Schenk (1998) reported on earlier tests using the same dataset. The paper ends with conclusions and an outline of future research. 2. BACKGROUND 2.1. Object recognition paradigm At the heart of the paradigm is the recognition that it is impossible to bridge the gap between sensory input data and the desired output. Consider a gray level image as input and a GIS as the result of object recognition. The computer does not see an object, e.g., a building. All it has available at the outset is an array of numbers. On the output side, however, we have an abstract description of the object, for example, the coordinates of its boundary. There is no direct mapping between the two sets of numbers. A commonly used paradigm begins with preprocessing the raw sensory input data, followed by feature extraction and segmentation. Features and regions are perceptually organized until an object, or parts of an object, emerge from the data. This data model is then compared with a model of the physical object. If there is sufficient agreement, the data model is labeled accordingly. In a first step, the sensor data usually require some pre-processing. For example, images may be radiometrically adjusted, oriented and perhaps normalized. Similarly, raw laser altimeter data are processed to 3-D points in object space. The motivation for feature extraction is to capture information from the processed sensory data that is somehow related to the objects to be recognized. Edges are a typical example.

Access restriction

Copyright

fullscreen: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation