Full text: Proceedings, XXth congress (Part 7)

ul 2004 
l-scale 
> third, 
omplete 
? image 
assified 
se" À 
robust 
basing 
'eatures 
coarse 
1 of the 
Ires, in 
ver the 
ullt-up 
fields. 
form in 
fled to 
, other 
ned on 
; to the 
ouring 
eristics 
coarse 
mation 
ovided 
: initial 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
4. RESULTS 
The rule base developed on the basis of small subsets is applied 
to each of the three flight track images to determine the 
transferability of the classification scheme. The outcome is 
validated by comparing the classification to the reference GIS 
data set and the aerial photographs based on 500 randomly 
distributed control points. Since the vector data isn't available 
for all of the area covered, the accuracy for the respective 
regions is determined solely by a comparison to the aerial 
photographs. 
The result shows that the built-up areas can be identified with 
an overall accuracy of 86% for track 1, 85% for track 2 and 
91% for track three. The classification for flight track 1 is 
illustrated in Figure 3 along with the reference vector data on its 
right. It can be seen that the main body of the settlements — 
shown red in the reference map - is detected very accurately. 
These areas could be classified with an accuracy of more than 
90%. This promising result can be traced back to the fact that 
these zones contain quite characteristic features like high 
texture, significant spectral difference of the single structures to 
their neighbours and the existence of multiple strong scatterers 
and shadow areas. In this manner even recently built-up 
development areas could be identified, which are not yet 
included in the reference data (see blue arrows in Figure 3). 
Errors occurred mainly in the context of highly structured areas 
possessing diverse strong scatterers and significant shadow, e.g. 
non-urban street crossings surrounded by groups of trees. 
The most significant false assignments occurred in recreation 
areas or allotments flanking the settlements. These zones - 
represented by greenish colours in the reference map - could 
only be identified when featuring significant texture along with 
the existence of some bright scatterers. Those spots situated in 
the far range region of the image lack these conditions due to 
the decreasing influence of surface roughness in far range. 
Consequently, these areas appear as dark, smooth zones without 
significant scatters. Thus, they can not generally be separated 
from areas in mid- and near-range featuring a significantly 
lower meso-scale roughness in reality. In mid- and near range 
the recreation areas and allotments possess the same 
characteristics as smaller forest stands, groups of trees or rough 
agricultural fields — consequently these zones remain 
unclassified. 
S. CONCLUSIONS 
The study has shown the applicability of high resolution, single 
polarised X-band imagery for the detection of built-up areas. 
Nevertheless, the significant variation of radar backscatter 
along the surface of a single structure (e.g. a building), the 
interaction of the scattered waves with multiple objects, the 
range-dependant effects of surface roughness and the visibility 
of objects subject to the line of sight result in considerable 
ambiguities of the recorded signature. Thus, the intensity 
information has to be analysed in consideration of its spatial 
context including the attributes of the surrounding objects as 
well as the characteristics of the super ordinate structure instead 
of solely regarding the backscatter signal. The object-oriented 
approach has proven to be very efficient as it provides multiple 
tools and features to address textural, contextual and 
| hierarchical properties of image structures. 
481 
6. FUTURE PERSPECTIVES 
A first future goal is the improvement of the presented 
classification scheme in view of a more accurate detection of 
recreation areas, parks and allotments. In addition the 
development of comparable procedures for single-polarised C- 
and L-band imagery is designated. 
A second field of study will be dealing with the potential 
improvements associated with an analysis based on a 
combination of single-polarised X- and L-band data (multi- 
frequency analysis). Finally the integration of polarimetric 
decomposition properties considering the quad-polarised L- 
band data is planned. 
REFERENCES 
Baatz, M., Schápe, A., 1999, Object-Oriented and Multi-Scale 
Image Analysis in Semantic Networks. In: Proc. of the 2nd 
International Symposium on Operationalization of Remote 
Sensing, August 16-20, 1999. Enschede. ITC. 
De Kok, R., Wever, T., Fockelmann, R., 2003. Analysis of 
urban structure and development applying procedures for 
automatic mapping of large area data. In: Carstens, J. (Ed.): 
Remote Sensing of Urban Areas 2003, 41-46. 
Ehrlich, D., Lavalle, C., Schillinger, S., 1999. Monitoring the 
Evolution of Europe's Urban Landscapes. In: Proc. of the 
IGARSS'99 in Hamburg. Vol. III, pp. 2705-2707. 
Forster, B.C., Ticehurst, C., 1994. Urban Morphology 
Measures from Optical and RADAR Remotely Sensed Data — 
Some Preliminary Results. Proc. ISPRS Commission VII 
Symp., Sept. 26-30 Rio de Janeiro, Brazil, Vol. 30, Part 7b, pp. 
291-296. 
Haack, B., 1984. L- and X-Band Like- and Cross-Polarized 
Synthetic ^ Aperture Radar for Investigating ^ Urban 
Environments. Photogram. Eng. & Remote Sensing, Vol.50, 
No.10, pp. 1471-1477. 
Haralick, R. M., 1979. Statistical and structural approaches to 
texture. Proceedings IEEE, Vol. 67, no.5, pp. 786-803. 
Henderson, F.M., Xia, Z.G., 1998. Radar Applications in 
Urban Analysis, Settlement Detection and Population Analysis. 
Principles and Applications of Imaging Radar (F.M. Henderson 
and A.J. Lewis, eds.), Chapter 15. New York, pp. 733-768. 
Hofman, P., 2001. Detecting urban features from IKONOS 
data using an object oriented approach. In: Remote Sensing & 
Photogrammetry Society (Ed.): Proceedings of the First Annual 
Conference of the Remote Sensing & Remote Sensing Society, 
28-33. 
Kressler, F., Steinnocher, K., 2001. Monitoring | urban 
development using satellite images. In: Jürgens, C. (Ed.). 
Remote Sensing of Urban Areas. Regensburger Geographische 
Schriften, Heft 35, 140-147. 
Kressler, F., Steinnocher, K., Kim, Y., 2002. Urban land cover 
mapping from Kompsat EOC panchromatic images using an 
object-oriented classification approach. In: Proceedings of the 
Third International Symposium Remote Sensing of Urban 
Areas, Vol. 1, ISBN 975-567-219-X, pp. 219-226, Istanbul, 11- 
13 June, 2002. 
 
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.