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FutureFuture
DiscussionDiscussion
IntroductionIntroduction
MethodologyMethodology ResultsResultsAbstractAbstract
There are three types of data used in the project. They are IKONOS,
ASTER, and Landsat TM, representing high to low spatial
resolution between 4 meters and 30 meters. The acquisition date
of the IKONOS data is October 14, 2000. The four bands used
are blue (0.45-0.53 μm), green (0.52-0.61 μm), red (0.64-0.72
μm ) and near infrared (0.77-0.88 μm ) at 4-meter resolution.
Landsat TM data were acquired on May 26, 1996. The ASTER
image was acquired on July 29, 2000 and has three bands, two of
which are visible and one of which is near infrared bands at 15-
meter spatial resolution. The three bands used are 0.52-0.60 μm,
0.63-0.69 μm and 0.76-0.86 μm.
A series of image-preprocessing operations were performed to
ensure the proper registration and the compatibility of the images.
Neural network classifier is the main target to be examined. A
standard maximum likelihood classifier was used as reference.
k
In line with the object-oriented approach in the development of the Amazon Information
System, an object-based neural network classifier is implemented with the new system
architecture. This research compares the performance of a neural network classifier to that
of a conventional classifier. The project analyzed three images at different spatial resolutions
to examine the results from the two classifiers on images at different scales. The data
subsets used are from IKONOS (4 meters), ASTER (15 meters), and Landsat TM (30
meters). The data were acquired in Altamira, Brazil, a typical eastern Amazon tropical area
with a collage of cultivated land, forest, river,and city. A series of pre-processing procedures,
such as registration and cloud masking, were applied to assure that the actual subsets cover
exactly the same area. Research results confirm that a neural network classifier, using
multiple source data, yields superior results compared to a maximum likelihood classifier.
The object-oriented approach to the implementation adds flexibility in interface, interaction,
versioning, and porting. Future studies will focus on the development of a parallel-based
strategy to shorten training time and the time for constructing alternative neural networks.
Performance of an Object-Based Neural Network Classifier on Land Cover Characterization in Amazon, Brazil
An object-based neural network constructed upon the principle of a multiple layered
backpropagation perceptron was implemented in the Amazon Information System, with
open options. This paper tests if the neural network satisfies the functional requirements
and demonstrates a potential for superior classification capability compared to conventional
digital image classifiers, such as the maximum likelihood. The following are the main
objectives:
Testing the effect of changing the number of neurons used, learning rate, and training
samples, to guide the optimization of the classifier design and operation.
Comparing the performance of a neural network classifier to other classifiers, e.g.
maximum likelihood, to see if the neural network is superior in tropical land cover
characterization.
Examining the neural network classifier with satellite images at multiple scales, or
multiple spatial resolutions, in extracting land cover features.
A. Hidden Units
Given a set sample, the accuracy of the
land cover characterization changes
slightly with the number of hidden units.
In general, over-structured or under-
structured neural networks show defects.
1 (under-structured)
8 (properly-structured)
150 (over-structured)
B. Hidden Layers
The relationship between hidden layers and the accuracy is
similar to those of between hidden units and the accuracy. In
other words, over-structured (over-fitting) or under-structured
neural networks may occur.
3 (over-structured)
2 (slight over-structured)
1 (properly in this case)
C. Training Samples
160 pixels 350 pixels 1100 pixels
NN MLC NN MLC NN MLC
The following can be noticed from this set of images.
• Neural networks (NN) are superior to maximum likelihood classifiers in accurately detecting land cover features. This is especially true when the
training samples are limited. Note the incorrect classification of water surfaces by MLC in the first two cases, in contrast to these by NN.
• The accuracy of NN classifier has less to do with the number of training samples than with the proper training sample.
• NN may be over-trained. Feeding correct training samples to neural network classifiers is important for achieving desirable accuracy.
NN MLC
NN MLC
NN MLC
IKONOS (4m)
ASTER (15m)
Landsat TM (30m)
E. Scale Effects
0.10.3
0.5
0.7
0.9
21
23
2424
37D. Learning Rate
The neural network system converges faster to
the expected overall error for the network with
a higher learning rate.
Further study with the neural network classifier will focus on better pre-processing,
training optimizing, complicated applications and hybrid classifier.
A multiple layered backpropagation/feedforward classifier was implemented with object-
oriented programming, which gives the flexibility to construct a variety of neural network
architectures. The performance of the classifier was examined internally and externally.
Internally, the classifier is applied with different hidden units, hidden layers, learning
rate, and training samples. Externally, it is compared with a standard maximum
likelihood classifier and with multiple scale satellite images.
The experimental land cover/use classification with the neural network classifier shows:
The number of hidden units and layers affects the accuracy significantly. Both over-
structured and under-structured neural networks can occur.
The increase of the learning rate reduces the learning time, but degrades the overall
accuracy, especially secondary succession in the study area.
The size of training samples does not affect significantly the accuracy of the
classification.
The classification accuracies using a neural network classifier are better overall than
using a maximum likelihood classifier. The neural network is especially superior when
few training samples are available.
Neural networks work well with multiple scale satellite images.
IKONOS Landsat TMASTER
IKONOS (Left: true color; Right: false color)
The study area is located between W52º31'5", S2º59'25" and
W52º3'3", S3º30'16". This is a typical tropical area in the east
Amazon, Brazil, where a collage of cultivated land, pasture,
forest, succession features, river, and city exists. These
features are used in the comparison of land cover classification.
NN is more consistent in accuracy over
scales. NN did a better job than MLC
at all three scales in this context.
Genong (Eugene) Yua1, Ryan R. Jensena2, Paul W. Mausela3, Eduardo S. Brondiziob4, Emilio F. Moranb5, and Vijay O. Lullaa6, a. Department of Geography, Geology and Anthropology, Indiana State University, Terre Haute, IN 47807, USA; b. ACT/Department of Anthropology, Indiana University, Bloomington, IN 47405, USA.
(Emails: 1. [email protected]; 2. [email protected]; 3. [email protected]; 4. [email protected]; 5. [email protected]; 6. [email protected].)
ReferencesReferences
• Duda, R.O., Hart, P.E., and Stork, D.G., (2001), Pattern Classification. John Wiley &
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• Paola, J. and Schowengerdt, R. A., (1995). A review and analysis of backpropagation
neural networks for classfication of remotely-sensed multi-spectral imagery. International
Journal of Remote Sensing, 16: 3033-58.
• Foody, G. M., and Arora, M. K. (1997), An evaluation of some factors affecting the
accuracy of classification by an artificial neural network. International Journal of Remote
Sensing, 18(4):799-810.
• Bishop, C.M., (1995), Neural Networks for Pattern Recognition. Oxford: Clarendon
Press; New York: Oxford University Press, 1995. 482p.