Znaczenie pola powierzchni i długości obiektów w półautomatycznej klasyfikacji obiektowej użytków zielonych na zdjęciach satelitów serii LANDSAT The influence of area and length of objects in semi-automated object classification of grasslands on LANDSAT images
Semi-automatic method for object classification of the
grassland procedure involves two stages: 1) the creation of
image segments as a representation of natural spatial complexes,
2) classification of the segments. So far, the classification
algorithms were used refer to the three categories of
characteristics: spectral, panchromatic or geometric.
In the first stage of the work segmentation were performed
of the composition of the two satellite images Landsat7 acquired
at different seasons of the year: in September 1999 and
the beginning of May 2001. Panchromatic data were used for
distinguishing complexes due to the greater (in comparison
with spectral data) spatial resolution. In the area of grasslands
landscape-vegetation complexes (Matuszkiewicz, 1990,
Kosiński, Hoffmann -Niedek, Zawiła, 2006) were distinguished
of approximately a hundred to a few hundred meters in length
and of about 20 ÷ 200 panchromatic image pixels. Semi-automated
delimitation of complexes were carried out under the
visual control, using as auxiliary material aerial photographs
and topographic maps.
In the second stage (classification of segments) an attempt
were taken to assess the suitability of selected geometrical
features to distinguish grasslands in use (currently or potentially)
from grasslands unfit for production use due to excessive
or insufficient moisture. The classification algorithm used
GIS tools for measuring area and length of segments and
artificial neural networks as a tool for classification.
The previous studies of the Piotrkowska Plain show that
the complexes of meadows used differ from those abandoned
in terms of size and shape of objects (Kosiński, Hoffmann-
Niedek, 2006, Fig. 1). Hypothesis that area and length of the
landscape -vegetation complex are cues of identification in relation
to the use and moisture of grasslands.
43 complexes of the grassland have been established as
training samples on the Piotrkowska Plain in the Pilsia valley.
In order to avoid overfitting classification algorithm to data
from the Piotrkowska Plain, in order to allow the application
of the algorithm for another mezoregionu 10 complexes have
been selected as a validation set in the Szczercowska valley.
To evaluate the classification results 32 complexes have been
collected from Szczercowska Basin (test set). All treining set
objects were described in terrein. Validation and test set objects
were classified by a more accurate metod (based on biteporal
image: Kosiński, Hoffmann -Niedek, 2008) and checked
at random in the field. Objects of learning, validation and test
set have been grouped into five categories according to use
and habitat moisture (Kosiński, Hoffmann -Niedek, 2008; Table
1). For learning neural networks fife categories of objects
of the learning and validation set were generalised into the
three classes.
In the Szczercowska Valley combination of characteristics
(area and length) of the abandoned complexes is more close
to the meadows in use than on the Piotrkowska Plain (Table
2). Therefore, the classification algorithm of the Piotrkowska
Plain can not be directly applied to Szczercowska Basin. To
obtain the correct result of classification, the classes of test
set has been interpreted differently than in the learning and
validation sets (Table 3, Figure 2). In the test sample 3/4 of
the 23 complexes of meadows potentially used were classified
correctly, while of nine abandoned ones due to unfavorable
moisture habitats correctly classified 2/3. Thus confirmed the
working hypothesis.
Application of artificial neural networks can cancel the
designation of non parametric empirical indicators of the size
and shape of the complexes (Fig. 1). Neural networks auto-uwilgotmatically
builds a morpfometric model based on simple indicators
such as area and length of the object.
Two model types of artificial neural network have been
tested: 1) multilayer perceptrons (MLP) wich use hyperplanes
to divide up feature space, 2) radial basis function network
(RBF) wich use hyperspheres. MLP networks have proved to
be more suitable to build the model than the RBF network.
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