ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
ISPRS, Vol.3- 
32 
PER-FIELD CLASSIFICATION INTEGRATING VERY FINE SPATIAL 
RESOLUTION SATELLITE IMAGERY WITH TOPOGRAPHIC DATA. 
Mauro CAPRIOLI, Eufemia TARANTINO 
POLYTECHNIC UNIVERSITY OF BARI 
Dept, of Highway and Transportation 
Via Orabona 4, 70125 BARI - ITALY 
Tel.: +39 080 5569387 - Fax: +39 080 5569329 
E-mail: caprioli@dvt005.poliba.it; tarantino@dvt005.poliba.it 
Keywords: Satellite Imagery - Topographic Data - GIS - Per-field Classification 
ABSTRACT 
The paper analyses an automated system for classifying land use on a locale scale, integrating Remote Sensing and GIS data, with the 
aim of extracting meaningful information in performing spatial analysis in urban contexts. 
As a first step, the backgrounds in extraction of contextual information from fine spatial resolution satellite imagery in an integrated 
RS/GIS system are considered in detail. 
Finally, a per-field classification technique, applied to an IKONOS multispectral image with spatial resolution of 4 m by utilising a digital 
topographic map, is described and the accuracy assessment of classification with the nature of problems emerged in the procedure is 
clearly identified. 
1. INTRODUCTION 
The information contained in digital imagery, acquired by 
Remote Sensing technology, can be used for mapping, 
monitoring and assessing the properties of the environmental 
and territorial feature elements. Of all these three main 
application fields in Remote Sensing, making thematic 
cartography by means of automatic classification methods is 
surely among the most widespread and in many cases is an 
essential preliminary step for further applications. 
Up to now spatial resolution of data given by earth observation 
satellites has proved inadequate in providing detailed 
topographic peculiarities, in specific application domains of 
analysis and monitoring of urban environment. Modality of 
terrestrial phenomenon representations, augmented geometric 
accuracy, temporal flexibility of acquiring, spatial land cover and 
appropriate use in spatial modelling terms suggest, instead, the 
diffusion and continual use of new very fine spatial resolution 
satellite sensors imagery, as data sources for spatial analysis in 
urban contexts. 
The high level of investigations enabled by interacting with other 
disciplinary sectors, can support planners’ activities better, as 
Mesev, Longley and Batty (1996) have argued: “our concern 
with land use revolves around the central 'urban/non urban' 
dichotomy as manifest through physical form, although in 
practice finer disaggregations as well as measures of the 
intensity of human activities are also desirable". 
The informational classes of a thematic mapping are not directly 
registered, but must be derived indirectly by using evidence 
contained in the spectral data of an image. When we apply 
standard procedures of per-pixel multispectral classification the 
increase of spatial resolution leads to augmentation in ambiguity 
in the statistical definition of land cover classes and a decrease 
of accuracy in automatic identification. This problem may be 
overcome by means of per-field classification techniques which 
involve analysing groups of pixels within land cover parcels. 
Such technique, based on the integration of remotely sensed 
imagery and digital vector data, has been used to generate land 
cover and land use information for more than a decade 
(Carbone, Narumalani and King, 1996; Ehlers, Greenlee, Smith 
and Star, 1991; Hinton, 1997;). Innovation and power of recent 
GIS platforms and analytic flexibility of Image Processing 
softwares make the integration of satellite data with numerical 
and scaled topographic data much more feasible, and this can 
lead to an increase in accuracy of the classification compared 
with the per-pixel technique, as well as improvements in 
interpretations of results with incorporating spatial variability and 
texture inherent in fine spatial resolution imagery. 
This work investigates a per-field classification methodology, 
applied to an IKONOS multispectral image with spatial resolution 
of 4 m by utilising a digital large scale topographic map as a 
representative reference land cover, and assesses the accuracy 
of classification by comparing the results carried out for both the 
per - pixel and the per - field techniques. Finally, the nature of 
problems emerged with both procedures is clearly identified. 
2. THE EXTRACTION OF CONTEXTUAL INFORMATION 
FROM FINE SPATIAL RESOLUTIONSATELLITE IMAGERY 
IN AN INTEGRATED RS/GIS SYSTEM. 
The accuracy with which land use has been mapped up to now 
from satellite sensor imagery from local to national scales has 
been limited by the relatively coarse spatial resolution of 
instruments. For example, for the land cover maps generated 
using Landsat Thematic Mapper (TM) imagery, with a spatial 
resolution of 30 m, a considerable amount of detail in the scene 
is obscured from the image. The availability of recent 
multispectral imagery with very fine spatial resolution has 
increased our ability to map land use in geometric detail and 
accuracy Aplin, Atkinson and Curran, 1997) for local and 
national scale investigations. 
However, these sources of imagery are likely to generate other 
problems. Even if the radiometric resolution is enhanced (11 bit 
for IKONOS imagery), spectral capabilities are generally limited 
compared to those of the previous generation sensors (seven 
bands for the Landsat TM). Moreover, associated with an 
increase in spatial resolution there is, usually, an increase in 
variability within land parcels ('noise' in the image) generating a 
decrease in accuracy of land use classification on a per-pixel 
basis (Townshend, 1992). 
Traditional automated classification techniques classify land use 
on a basis of spectral distribution of the pixels within an image, 
whereby each pixel is associated with the most similar spectral 
class. This general method can produce results that are 'noisy' 
due to the high spatial frequency of the land covers. 
The alternative technique of per-field classification (so called 
because fields, as opposed to pixels, are classified as 
independent units) takes into account the spectral and spatial 
properties of the imagery, the size and shape of the fields and 
the land cover classes chosen. 
In fact, this approach requires a priori information about the 
boundaries of objects in the image, for examples, roads fields. If 
the boundaries of these fields are digitised and registered to the 
image, then some property of the pixel lying within boundaries of 
the field can be used to characterise that fields. For instance, 
the means and standard deviations in the four IKONOS bands of 
pixel lying within roads fields could be used as features defining 
the spectral reflectance properties of fields. Normally, the use of 
map and image data would take place within a geographical 
information system (GIS), which provides facilities for 
manipulating digitised boundary lines (for example, checking the 
set of line to eliminate duplicated boundaries, ensuring that lines 
'snap on’ to nodes, and identifying illogical lines that end 
unexpectedly). 
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