Peer Reviewed Articles
485-491 Rule-Based Classification of Water in Landsat
MSS Images Using the Variance Filter
Paul A. Wilson
Abstract
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The variance filter is a textural algorithm capable of distinguishing between
flat, uniform water bodies and cloud or mountain shadows when applied to
satellite imagery. The filter output forms the basis of rules used by a knowledge-based
classifier, which segments water adaptively. The use of the filter in the
unsupervised classification of water is demonstrated on two spectrally varied
Landsat MSS images. The same images are segmented using a conventional thresholding
algorithm. The two algorithms identify a similar proportion of the water
pixels in both images; however, the rules-based algorithm does not generate
any false positives, whereas the threshold algorithm misclassifies many shadow
pixels as water. The rules-based algorithm is less efficient at finding small
lagoons and swamps than at finding large water bodies.
Detecting Subpixel Woody Vegetation in Digital Imagery Using Two Artificial
Intelligence Approaches
Patricia G. Foschi and Deborah K. Smith
Abstract
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Small strips or patches of woody vegetation, typical landscape
elements in many farming areas, are frequently not detected
by standard computer-assisted clessification of digital
satellite imagery because such landscape elements are
smaller than the pixel size and are mixed with other classes.
This study essentially compares two artificial intelligence opproaches -
machine-vision and neural-network rnethods - developed
to improve classification accuracy for this mixed
pixel problem. Simulated multispectral and panchromatic
SPOT HRV imagery of lowland Britain was used to test both
methods. Compared to standard supervised multispectral
classification, both methods yield significant improvements
in detecting subpixel woody vegetation. In general. the machine-vision approach outperformed the neural-netwark approach.
However, because each method generated different
types of misclassifications, a classification map representing
only the woody vegetation found by both methods provided
the results with the least amount of overall error.
Performance of a Neural Network: Mapping Forests Using GIS and Remotely Sensed
Data
A.K. Skidmore, B.J. Turner, W. Brinkhof, and E. Knowles
Abstract
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Neural networks have been proposed to classify remotely
sensed and ancilliary GIS data, In this poper, the back propagation
algonithm is critically evaluated using as an example,
the mapping of a eucalypt forest on the far south coast of
New South Wales, Australia. A GIS database was combined
with Landsat thematic mapper data, and 190 plots were field
sampled in order to train the neural network model and to
evaluate the resulting classifications. The results show that
the neural network did not accurately classify GIS and remotely
sensed data at the forest type level [Anderson level
III], though conventional classifiers also perform poorly with
thiis type of problem. Previous studies using neural networks
have classified more general (e.g., Anderson Level I, II) landcover
types at a higher accuracy than those obtained here,
but mapped land cover into more general themes. Given the
poor classification results ond the difficulties associated with
the setting up of suitable parameters for the neural-network
(backpropagation) algorithm, it is concluded that the neural network approach does not offer significant advantages over
conventional classification schemes for mapping eucelyptus
forests from Landsat TM and ancillary GIS data at the Anderson
Level III forest type level.
Texture Analysis of Tropical Rain Forest Infrared Satellite Images
Robert Riou and Frederique Seyler
Abstract
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This paper describes a new application of the Fourier transform
for texture analysis of low reflectance, rain forest satellite images. After correction of edge effects, angular power
spectra have been analyzed on windows of 64 by 64 pixels in
size. Results of spectra analysis are compared to visually extracted
linear features of near infrared SPOT images from
South Cameroon forested areas. This method allows for the
detection of low-contrast, periodic luminance variations and
the extraction of lineament direction with an accuracy of
100. An algorithm of processing of the whole image aimed at
regional studies of texture networks is proposed. Applications
to structural geology, forest-type discrimination, and
soil studies in the tropics are briefly evaluated.
523-533 Multisource Classification of Complex Rural Areas
by Statistical and Neural-Network Approaches
L. Bruzzone, C. Conese, F. Maselli, and F. Roli
Abstract
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The usefulness of spectral (Landsat-5 Thematic Mapper images), texture (grey-level
co-occurrence matrix statistics), and ancillary (terrain elevation, slope,
and aspect) data to characterize two complex rural areas in central Italy
is quantitatively demonstrated. A statistical and a neural- network classification
approach are applied to such a multisource data set, and their classification
performances are assessed and compared. The classification performances of
the two approaches are quantitatively evaluated in terms of global and conditional
Kappa accuracies. The Zeta statistics is used to evaluate the statistical
significance of the different classification accuracies obtained by the two
approaches by using multisource data.
535-544 The Effect of Neural-Network Structure on a Multispectral
Land-Use/Land-Cover Classification
Justin D. Paola and Robert A. Schowengerdt
Abstract
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While neural networks are now an accepted alternative to statistical multispectral
classification techniques for remote sensing image classification, the network
approach presents both unique challenges and abilities. The size of the hidden
layer must be determined by trial and error, and the random initial weight
settings result in different paths for the training procedure, making the
network a non-deterministic classifier. For the sample classification presented
here, it was found that there was a range of optimal hidden layer sizes below
which the occupancy decreased and above which the training time increased.
However, it was also found that, for a fairly wide range, the hidden layer
size made little difference to the final classification accuracy. Initial
weight randomization was as much of a factor as hidden layer size. Using
3 by 3 windows of data in each band was found, despite increased training
time per iteration, to achieve similar accuracy with less overall training
time, although with less consistency.
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