Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 
Comparison of classification results of original 
and preprocessed satellite data 
Barbara Kugler & Rüdiger Tauch 
Department of Photogrammetry and Cartography, Technical University of Berlin, FR Germany 
ABSTRACT: The production of satellite image maps is one of various objectives of image processing. Satellite image maps 
are very important tools as orientation maps in regions, where other topographic maps are either not available or out of date. 
They may also serve as basic information for thematic maps for landuse, geological and ecological purposes. 
The methods of multispectral classification are applied to obtain landuse maps from satellite image data. These methods 
are based on the characteristic spectral reflectance of objects that enables to extract feature sets for different classes. By 
means of the feature sets it is possible to assign every picture element (pixel) to one of the various classes. For digital image 
classification the spectral signatures are the substantial source of information. Therefore usually roughly preprocessed data 
are used. The classification of preprocessed data offers advantages if the concerned area is covered by several satellite 
image scenes. In this case the data undergo radiometrical and geometrical processing before classification takes place. 
The purpose of this paper is to investigate digitally classified original and preprocessed data. The classification results of 
the two data types are compared. 
1 INTRODUCTION 
During the last years many remote sensing techniques were 
developed. The satellite-scanned-data is used as basic 
information for many applications like the production of 
image and thematic maps for urban planning, land resource 
management such as agriculture, etc. All applications 
require several stages of processing until satisfying results 
can be obtained. For the production of image maps it is 
necessary to take regard of radiometric and geometric 
effects, which are caused by sensors or spacecraft. There 
fore digital methods are developed, which take account of 
these disturbances. 
Radiometric corrections such as contrast enhancement, 
corrections of pixel-and line-interferences, destriping etc. 
improve image quality and are prerequisite for further 
steps of processing. 
In order to compile maps with high accuracy it is 
essential to relate the satellite-scanned-data to the ground 
coordinate system. For a comparison of data from 
different dates or sensors the geometric relation to each 
other must be guaranteed. These problems can be solved 
with rectification and resampling algorithms. 
For other purposes like landuse or forest damage investi 
gations multispectral classification is used in order to gain 
the desired information. The digital methods of image 
classification are based on the spectral reflectances of 
objects. In the images, these spectral reflectances are 
represented through the grey-values in every spectral 
channel of each pixel. Every kind of radiometric and 
geometric image processing methods influence these values 
in a different way, with the consequence that the feature 
sets for the different classes change. This causes an 
alteration of classification results. For that reason normal 
ly roughly preprocessed data is used. But sometimes it is 
advantageous or even necessary to deal with better prepro 
cessed data. For a supervised classification a contrast 
enhancement in every spectral channel makes the inter 
active definition of training areas easier. The multitempo 
ral or multisensoral classification assumes already rec 
tified data. Otherwise it is not possible to assign picture 
elements, which represent the same ground area, to each 
other. In the case of the multitemporal data the different 
dated images do not match together identically, in the case 
of data from different sensors there are possibly differ 
ences in the image resolution. All these influences on grey- 
values caused by preprocessing make it necessary to inves 
tigate classification results of data, which is preprocessed 
in different ways. 
In the following some usual image processing methods 
are described briefly. After processing the digital data 
with linear contrast enhancement and/or rectification and 
resampling with the methods of nearest neighbourhood and 
bilinear interpolation, the obtained images serve as input 
material for supervised multispectral classification. The 
classification results and their comparisons are presented 
subsequently. 
2 CONTRAST ENHANCEMENT 
Normally satellite-scanned-data are available as digital 
data on CCT's. The quality of the original data is often 
very poor. The images are dark and have only low con 
trasts, which make a visual interpretation difficult. There 
fore the first step of preprocessing is a contrast enhance 
ment for every channel separately. The distribution of the 
grey values of original data only takes a small part of the 
possible values between 0 and 255. A linear contrast 
enhancement allows to stretch the values within the whole 
possible interval. The results are better in contrast and as 
a whole in the image quality. Other radiometric prepro 
cessings are often necessary; however our investigations 
are restricted to the effect of a linear contrast enhance 
ment. 
3 RECTIFICATION AND RESAMPLING 
For many applications it is necessary to get a relation 
between the satellite-scanned-data and the map grid. This 
can be achieved by a rectification and resampling of the 
data. The rectification can be realized with the help of 
transformation polynomials, where the coefficients are 
evaluated by a least squares adjustment. For the compu 
tation of the grey-values of the new pixels the following 
resampling algorithms are possible: nearest neighbourhood, 
bilinear interpolation or cubic convolution. The last two 
methods determine the new values by considering the 
surrounding pixels, with the result of artificial grey-values, 
whereas the first method takes only values, which already 
exist in the original image. 
Some investigations (Kahler, Milkus, 1986) show, that a 
row- and column-doubling of the image before rectification 
and resampling improves the result, especially the contrast 
of small linear features. This means that the resolution has 
been artificially enlarged. One of our case-studies investi 
gates wether such a data handling will be meaningful for 
classification of rectified images.