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THE AUTOMATIC CLASSIFICATION OF B&W AERIAL PHOTOS 
L. Halounova 
Remote Sensing Laboratory, Faculty of Civil Engineering, Czech Technical University Prague 
Thakurova 7, 166 29 Prague 6, Czech Republic, halounov@fsv.cvut.cz 
KEY WORDS: Land Cover, Photography, Texture, Fuzzy Logic, Segmentation, Aerial, Multiresolution 
ABSTRACT: 
The paper shows possibility to classify B&W aerial orthophotographs and other monochromatic remote sensing data using image 
enhancement phase and object-oriented analysis phase for the automatic classification. 
The first phase enlarges the spectral signature space by channels calculated by image filtering (median filter and Gauss filter) and by 
texture measures. The combination of various filter sizes (texture measures) and kernel sizes (filters) enlarges the signature space 
allowing the following image segmentation and classification in two or three scale levels. The at least two level classification 
simplifies thematically complex aerial orthophotographs by dividing the photo into thematically more homogenous areas in the 
higher level. The lower level brings the final sought classes. which can be slightly corrected in the third (lowest) level. The 
eCognition software was used for the image segmentation and for the automatic classification. It is the first method of automatic 
classification of land cover of monochromatic remote sensing data bringing accuracy better than 80 per cent. 
1. INTRODUCTION 
The paper author was a responsible person for analyzing one 
part of a project of the Czech Ministry of Agriculture. The aim 
of the project was to find solutions for automatic information 
extraction from B&W aerial orthophotographs. The scale of 
these aerial photographs was 1: 23 000. Each othophoto was a 
result of mosaicking. The classical way of automatic 
classification is based on close spectral signatures or other 
signatures of pixels representing the same classes. This 
assumption is not valid in case of B&W photographs where 
different areas are formed by pixels with the same values and on 
the other side one class is formed by wide range digital values. 
The signature space for individual classes overlapped and could 
not have been used for their distinguishing in this state. No 
references were found to show solution for the similar 
monochromatic data type. Known image enhancement methods 
were applied to be obtained more separable class signature 
space for individual classes. The automatic pixel-by-pixel 
classification was excluded from the analysis and replaced by 
the object-oriented classification performed for segmented 
image data. 
Segmentation has been used by several specialists who applied 
various interpretation keys (Borisov et al., 1987, Jagtap et al., 
1994, Naesset, 1996, Zihlavník, Palaga, 1995). The 
segmentation used in this project was the Fractal Net Evolution 
Approach (FNEA) commercially introduced by Baatz and 
Schäpe (1999) incorporated in commercial software 
eCognition. 
The proposed method can be applied for large number of with 
relatively high level of automation. That was the project goal. 
The result of the project should be applied for the whole coun- 
try and therefore their image processing operability was neces- 
sary. 
2. METHODOLOGY 
There were two main tasks in the image processing. To enlarge 
signature space of individual classes to be separable was the 
first task. To perform the automatic classification formed the 
second task. 
2.1 Signature space enlargement 
Signature space enlargement was done by using two ways of 
new channel calculation. One way was the image filtering by 
low-pass filters where Median filter and Gauss filter were 
applied. They supressed local image unhomogeneities. They 
showed high correlation with original image data. That was the 
reason why they did not assure sufficient class separability. 
Channels calculated from two kernel sizes and repeatable 
filtering were used within the project. Channels filtered by 
Gauss filter were calculated for standard deviation equal to 2, 3, 
and 4. 
Another tool was necessary for the successful solution. Haralick 
functions were the tool as functions characterizing textures. 
There are more Haralick functions used in image processing. 
Different numbers of them were used for different level of 
classification detail. Haralick functions were tested for several 
window sizes. Window sizes depended on individual class 
member sizes. Smaller window sizes were useful for small 
resulting class members sizes. The tests showed that there were. 
no prevailing trends in a certain direction and that was the 
reason why all directions where relations between pixels are 
determined, were taken into account. All directions meant that 
differences between a pixel and a reference pixel were 
calculated for directions 0°, 45°, 90°, and 135°. These 
differences were used for GLCM (Grey Level Co-occurrence 
Matrix) and GLDV (Grey Level Difference Vector) calculations 
from Haralick functions. Mean, standard deviation and 
dissimilarity functions were chosen for the orthophoto 
classification defined in following expressions. Their window 
size were 5x5 pixels, 11x11 pixels, and 21 x 21 pixels.