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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
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. 
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. 
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 
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. 
2.1. 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.

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