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. g-yu@indstate.edu; 2. r-jensen@indstate.edu; 3. pmausel@scifac.indstate.edu; 4. Ebrondiz@indiana.edu; 5. Moran@indiana.edu; 6. Vijay_lulla@mama.indstate.edu.)

ReferencesReferences

• Duda, R.O., Hart, P.E., and Stork, D.G., (2001), Pattern Classification. John Wiley &

Sons, Co., 654p.

• 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.