International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
202 
enhanced with multispectral image values. Not only the mean 
value of the multispectral channels can be used; other measures 
derived from the original bands, like NDVI, LAI or sigma 
values, can be displayed. Object analysis opens up a new 
chapter in textural analysis of the panchromatic data. It offers 
the possibility to analyse the (relative) amount of panchromatic 
sub-objects per multispectral pixel-object (since a multispectral 
pixel represents an area of more than 400 m2, it is meaningful 
to treat each pixel as a separate object). For this purpose, the 
multispectral pixel layer is segmented in objects, each pixel 
receiving a unique code. Then, the panchromatic dataset is 
segmented, taking into account the borders of the multispectral 
pixels. In a typical example of alpine forest (Fig. 1), this would 
mean that a SPOT4 pixel of 23 x 23 m 2 contains 529 
panchromatic pixels of 1 x 1 m 2 . After a two level segmentation, 
the single SPOT pixel covering a dense coniferous forest area 
contains an amount of ± 35 sub-objects, representing the 
amount of crowns and shadow areas inside that multispectral 
pixel-object. Using the same tolerance parameters for the 
segmentation, the forest/road pixel contains only 25 objects. 
The amount of panchromatic objects depends on the user- 
defined tolerance parameters. Their relative amount is therefore 
more important than the absolute one. Texture analysis, which 
depends upon window based algorithms, such as the first and 
second moment filter techniques, can then be expanded with the 
relative number of sub-objects. This allows a mixed pixel 
analysis, where the operator directly sees the spectral behaviour 
of the multispectral channels and the spatial properties of the 
high resolution panchromatic data. This is very helpful for 
advanced mixed pixel analysis. 
3. EVALUATION 
The visual evaluation is still a reliable praxis in high resolution 
image classification. Confusion matrixes (Richards, 1992), 
which are used in pixel based analysis are not useful for object 
evaluation. 
In principle, an object classification is false or correct. When a 
particular object receives a false classification, the statistics of 
the area represented by that object-class can be seriously 
affected. The fuzzy logic functions can determine the chance per 
object of falling into a specific class. For example, two objects 
belong to class A. Object 1 has a chance of 96 % and object 2 a 
chance of 75 %. Furthermore, the chance of object 1 to belong 
to class B is 92 % and for object 2, 15 %. The difference 
between the first and second option is therefore also a powerful 
indication of reliability. The evaluation procedure shows how 
important it is for the operator to be an expert on the objects of 
interest. A forester needs to know about the ecology of his 
forest area and the parameters that influence spatial and spectral 
behaviour of the objects of interest. This is similar for the 
hydrologist and the geologist or any other field expert. Object 
classification using fuzzy logic expects a high input of the field 
expert to define the decision functions. This evaluation part of 
the image classification is an area where a lot of research still 
lies ahead. 
4. DISCUSSION 
Although the literature mentions the possibilities of object- 
based image analysis since a few decades (Kettig and 
Landgrebe, 1976), only latest-technology hardware, intelligent 
software and high resolution images can advance this concept. 
In practice, a Pentium II-like processor, >256 MB RAM and >6 
GB hard disks are needed to have acceptable speed levels for 
tests with large amounts of image objects. The limitations of 
hardware development have not been reached yet. Therefore, 
this field of study is practically driven by ‘getting the most out 
of the modem machine’. Other interesting ways of image 
classification should not be overlooked in the meantime. The 
interaction with object-based remote sensing data and GIS 
objects has a high potential. This concept needs a lot of 
attention and might offer possibilities for map updating. 
The object-based analysis can only be successful, if a proper 
automatic evaluation is accepted among RS specialists. The user 
is interested in high accuracy with a certain cost factor for a 
specific set of objects of interest. Lower overall accuracy is 
often acceptable for lower cost, but objects of interest might 
need high accuracy. That makes evaluation standards for image 
classification quite complicated, when object-oriented 
classification is used. Then, evaluation might also become user 
dependent. The increasing demand for the field expert 
knowledge, however, is a good development. 
5. CONCLUSIONS 
Rapid improvements in hardware capacity, clever software and 
modem sensors make it possible to realise ideas, which have 
been developing for decades among the remote sensing 
specialists. In practice, during the realisation of these ideas, new 
challenges appear. This leads to increasing demands for 
knowledge of ground truth. Ground truth involves not only the 
current state of the landscape but also an insight in the 
processes that formed and are forming the present scene of 
interest. Not the whole scene needs to be interpreted; areas of 
interest have to be analysed using the huge amount of 
information available in high resolution data. Deriving 
information from imaging data is often limited due to the 
associated high costs. Only automatic procedures can reduce 
these costs. Object-based analysis offers an interesting option to 
improve automatic information derivation from an ‘exploding’ 
amount of data from different sensors. 
The representation of multispectral data is based on three 
domains: image space, spectral space and feature space 
(Landgrebe, 1999). The terrain expert is very familiar with 
image space. Object-based analysis allows a direct link between 
the image space display and the database information on the 
spectral and feature spaces. This makes object-based software 
quite intuitive and user-friendly. 
The robust query possibilities, well known in GIS, should be 
used in the interpretation of imaging data. The automatic