1S
of
1S
n,
ad
id
es
1d
or
al
Figure 6. Landsat image; an example of result of Minimum
Distance classification, and final result after correcting
A quick analysis of validation, focused on impervious areas
detected, has been realized for evaluating the goodness of the
applied processes, by using a confusion matrix based on ground
truth region of interest. A plenty of ROIs were selected all over
the landscape under investigation, which was enhanced by
using a sharpen filter to get a better visualization. Considering a
single class which merges together the impervious categories
found, it was reached an overall accuracy of around 63%. This
value is due to an important amount of terrains which are still
mixing with urban areas, but further investigations will be
focused on differentiate those kind of soils.
2.5 First remarks
Actually it is really difficult, at this spatial resolution, to
discriminate different typologies of urban settlements and
certain bare soils. That is why we look for additional layers and
steps, in order to express the most effective land cover
composition of urban landscape, consistent with the scale of
analysis. The results of the work will be an important step, but a
new starting point, to redefine a more precise spectral library
and a possible different composition of the layers into the
images in order to achieve the better and faster outcomes.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
3. DEFINING URBAN CLASS CATEGORIES, BASED
ON MORPHOLOGICAL FEATURES
3.1 Overview
The development of GIS technologies has provided a variety of
analytical tools for analysis and management of landscape,
urban or natural. The ability to quantify the structure of a
territorial system is a basic requirement for the study of
environment and its changes over time. The quantitative metric,
based on descriptive indicators, provides a representative
database which allows analyzing the landscape, but the
interpretation of the indicators requires an adequate knowledge
of the geographical context but, most of all, of the phenomenon
under investigation. In this work, under the hypothesis that
urban settlements are the effect of a sum of different typologies
of morphological structure, we intend to automatically
discriminate three different types of texture: continuous,
discontinuous, and scattered.
3.2 Methodology
3.2.1 Post-processing the remote sensing result: Once
obtained the final dataset about impervious areas, we aim to
measure the degree of physical continuity of urban settlements
through the use of morphological features such as shape,
fragmentation, and density, in order to define strong and weak
relations between the composing parts of the urban texture.
After applying filters of clump and median it has been
converted, the result of remote sensing classification, in a
shapefile and exported to a GIS platform. Morphological
features for all the patches, which compose the landscape, will
be now synthesized through the use of synthetic indicators.
3.2.2 Morphological indices: Three synthetic indices have
been employed for this study: a Covering Index (1), that is the
percentage of total area of a single cell (4) occupied by the
urbanized area (a) resulting of the sum of all the patches in that
cell; the Fractal Dimension (2) which equals 2 times the
logarithm of the perimeter p; (m) of a patch, divided by the
logarithm of the area of the patch a; (m?); the Degree of
Landscape Division (3) resulting by the quadrate of the ratio
between the area of a patch a; and the entire urbanized area a,
in a cell.
n
2.4
E i=l
A
20253 p) Q)
CI (1)
i=l
n
Inq,
i=l
FD=
2
DLD=1 ME 3)
i=1 X tor
where a; = area of patch
a, = total urbanized area in a cell
pi = perimeter of patch
A = area of a square cell of 200m
3.2.3 Analysis and texture classification: The calculation has
been proportioned by using a grid with square mesh of 200m,
