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Title
Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Author
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
175
COMBINING SPECTRAL AND TEXTURAL FEATURES FOR MULTISPECTRAL
IMAGE CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS
H. He , C. Collet
Department of Geography, University of Fribourg, Perolles, 1700 Fribourg, Switzerland
hongchang.he@unifr.ch , claude.collet@unifr.ch
KEYWORDS: Artificial Neural Networks, Multispectral Image, Landcover and Landuse, Classification, Grey Level Co-occurrence
Matrix.
ABSTRACT
A new procedure for multispectral image classification was investigated in this study. This procedure incorporates textural features
into conventional per-pixel classification and is implemented with artificial neural networks. The grey level co-occurrence matrix for
textural measure calculation is derived from a supervised spectral classification based on three SPOT spectral bands (XS1, XS2 and
XS3). Four textural measures, contrast, entropy, angular second moment and inverse difference moment, are calculated based on the
grey level co-occurrence matrix. An artificial neural network using a variant of the back-propagation algorithm is applied to the
procedure.
The performance of the new procedure is assessed relative to that of a classification combining spectral features and textural
measures based on the grey level co-occurrence matrix from the near-infrared SPOT band and a per-pixel classification procedure.
The test results show that the classification accuracy with the new procedure can be improved by more than 10%, compared to the
other two classification procedures.
The study indicates that the textural measures based on the grey level co-occurrence matrix from the supervised spectral classification
can reveal more effectively spatial forms of landcover and landuse in multispectral images. However, an inappropriate window size
for textural feature extraction affects the fidelity of textural features. Window sizes of 5x5 and 7x7 can be considered as the
appropriate ones for landcover and landuse classification in this test area.
1. INTRODUCTION
With the advent of higher spatial resolution satellite images and
their wide application to landcover and landuse mapping,
conventional per-pixel classifiers present more and more
limitations for the data processing. This is mainly due to two
factors. Firstly, conventional classifiers assume that landcover
classes in satellite images follow some kind of specific
distributions, e.g. Gaussian distribution, but in fact they are
often spectrally complex and heterogeneous. Secondly, per-
pixel classifiers are based only on spectral features, ignoring the
spatial information in images.
Many efforts have already been devoted to overcome these
problems, and three procedures are representative among them.
The first incorporates textural features into image classification
(Haralick et al., 1973; Jensen, 1979; Marceau et al., 1990;
Baraldi and Parmigianni, 1990; Sadler et al., 1991). However,
this method is under dispute. Some studies indicated that it only
provides a relatively small and sometimes even no practical
improvement of the accuracy of landcover and landuse
classification in urban areas compared to the use of only
spectral features (Jensen, 1979; Baraldi and Parmigianni, 1990;
Sadler et al., 1991). There are two possible reasons for these
results: (a) the procedure used for extraction of textural features
is based only on a single spectral image; thus, textural measures
cannot completely reveal spatial forms of landcover and landuse
in multispectral images; (b) the method also depends on
statistical assumptions about class distributions. The second
procedure is a spatial (or contextual) reclassification technique
(Wharton, 1982; Gong and Howarth, 1992; Fung and Chan,
1994). As two subsequent classifications are implemented in
this scheme, errors from the two stages and their interaction
would probably result in a decrease in the overall classification
accuracy. The third procedure applies artificial neural networks
(ANNs) in multispectral classification (Bischof et al., 1992;
Paola and Schowengerdt, 1997). A continuously increasing
number of investigations is focused on this classification
procedure for three reasons:
- The procedure does not require any a priori knowledge of the
statistical distribution of the data, i.e., nonparametric
classification.
- It can classify landcover and landuse classes with any
distribution, i.e. not only Gaussian but also multimodal or
even disjoint. This means that the procedure is capable of
classifying classes with heterogeneous data and high spatial
variability.
- It is adaptable and robust.
Although this procedure results in high classification accuracy,
typically more than 80%, it is also necessary to combine textural
features into the procedure to fully exploit the potential of
ANNs. However, very few attempts have been made up to now
in this direction.