73 
Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
Per-field classification of a segmented SPOT simulated image 
J.H.T.Stakenborg 
Commission of the European Communities, Joint Research Centre, Ispra Establishment, Italy 
ABSTRACT: A per-field classification has been applied to a SPOT simulated image of an area in southern France 
with mainly agricultural landuse. Using edge preserving smoothing, edge extraction, and edge tracking 
techniques the field boundaries are derived from the panchromatic channel. The segmented image is replaced by 
a property table in which the spectral signatures, position, and simple form describing factors of all 
segments are stored. 
RESUME: Une méthode de classification 'par champs' a été appliquée à une image de simulation SPOT dans une 
région à prédominance agricole du sud de la France. Les limites de champs ont été déterminées sur le canal 
panchromatique par des techniques de lissage conservant les contours, puis l'extraction et le suivi de ces 
contours. On substitue à l'image ainsi segmentée une table d'attributs où sont mémorisées, pour chaque 
segment, les signatures spectrales, la position et de simples facteurs de forme. 
1 INTRODUCTION 
New satellite platforms provide us with more 
detailed information due to the higher spectral and 
spatial resolution. For research of the spectral 
properties of objects methods will be used which have 
proven their use in the past, only shrinked to a 
space with less dimensions. However higher spatial 
resolution will lead to detection of more objects in 
a scene. Analysing a satellite image becomes more and 
more a matter of spatial analysis. Research of the 
spatial relationship of objects to for instance 
landuse can not refer to established methods. Not 
many attempts have been made partly due to the fact 
that much effort has to be put in co-operation 
between computer scientists and experts. 
Only recent commercial software packages are 
distributed on the common market which contain the 
required basic processes needed for spatial analysis, 
therefore opening possibilities for experts to get 
involved in spatial analysis. When the development of 
methods for spatial analyses and "reasoning" gets a 
higher priority in research programs, better results 
in this field are to be expected. 
What I have tried in this direction is to design an 
'automatic' per-field classification system to 
analyse the spectral and spatial characteristics in a 
complex simulated satellite scene and learn from it. 
Automatic stands for the field boundaries detection 
by segmentation and is placed in quotation marks 
because parameters have to be fed to the system which 
can influence the result considerably. Image 
classification in this paper must be seen as a 
display of intermediate results. To learn from the 
analysis is more important. 
The image under investigation is a subset from a 
SPOT simulated image consisting of 231 x 216 pixels 
in the panchromatic band of the "Les Vans" area of 
the department of Ardeche in southern France(fig. 
la). It is part of a least favoured area as defined 
by the Commission of the European Communities. It is 
a rather flat area with mainly agricultural landuse 
and some outcrops of hard rocks surrounded by 
woodlands. It was previously investigated by Megier 
(1984) who with the help of digitized field borders 
tested some parameters for the per-field 
classification. These fields are used in the 
following to measure the performance of a 
classification and to analyse form parameters i.e. a 
measure for elongatedness, irregularity, and rotation 
of known fields. The SPOT satellite works well at 
this time and will produce images from the 'Les Vans' 
area soon. Probably this article is the last one 
dealing with simulated images. 
2 SEGMENTATION 
Segmentation of the image is required to obtain a 
list of polygons describing the fields of landuse. 
There are many ways to segment an image. 
The procedure to prepare an image for segmentation 
proposed here includes an edge preserving smoothing 
technique. Edge extraction produces an edge-map. This 
edge-map has to be cleaned before tracking. Edge 
tracking and labeling is needed for using the 
segments as individual and separated regions. 
Fully aware of the existence of better segmentation 
procedures I would like to propose a filtering method 
based on the detection of local features. Other 
methods have been tested. For the near future 
segmentation by contouring will be developed but at 
the time it is not ready. 
2.1 Edge preserving smoothing 
The edge preserving smoothing used is described in 
Stakenborg(1985). A short explanation will be given. 
Smoothing takes place with a 3 by 3 mask. 
Thresholding of the differences from the central 
pixel divides the kernel into a homogeneous and a 
non-homogeneous part consequently creating a bit 
pattern. If this bit pattern is found in a set of 
pre-defined edge patterns the pixel is left alone 
otherwise only the mean of the pixels under the 
pattern is taken. This is a slight improvement of the 
method proposed by Lev et.al.( 1977) . Threshold 
selection is critical. By defining a threshold range 
and counting the number of selected patterns from the 
set for each pixel and each threshold value, the 
'ideal' threshold can be determined and lies around 
the peak of the threshold histogram. 
Tests have shown that a threshold from 4 to 6 for 
the Landsat Thematic Mapper channel 3 and threshold 
10 for the SPOT simulated data channel 4 work well. 
Blockdiagrams in figure 2 show the effect of the edge 
preserving smoothing of the subset in figure la seen 
from the N-E. 
Other methods for edge preserving smoothing work