AD-A285 486 S~~~III iii UI lll iUmI Im i

US Army Corps

of Engineers

Cotr oEngeeng USACERL Special Report (SR) EC-04/28

PR esech Laboratories August 1994

Using Neural Networks To Correlate

Satellite Imagery and Ground-truth Data

by

Xiping Wu

James D. Westervelt

Current approaches to evaluating the condition of Army land managers need an alternate approach for

natural resources on Army training and testing lands correlating satellite imagery to ground-truth

are statistically based and attempt to generate measurements. This approach can be found in the

rational mathematical formulae that characterize the application of neural networks.

relationship between satellite imagery and ground- This report presents the research results of using

truth data. Even in the hands of a well trained neural networks as a computational tool to correlate

image processing expert, the application of standard ground-truth data with satellite imagery for

image processing tools can yield varying results. Hohenfels, Germany, and to turn the imagery into

maps of the installation.

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I August 1994 Final

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Using Neural Networks To Correlate Satellite Imagery and Ground-truth Data In-Laboratory

Independent Research

6. AUTHOR(S)

Xiping Wu and James D. Westervelt

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION

U.S. Army Construction Engineering Research Laboratories (USACERL) REPORT NUMBER

P.O. Box 9005 Special Report

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Approved for public release; distribution is unlimited.

13. ABSTRACT (Maximum 200 words)

Current approaches to evaluating the condition of natural resources on Army training and testing lands are statistically

based and attempt to generate rational mathematical formulae that characterize the relationship between satellite imagery

and ground-truth data. Even in the hands of a well trained image processing expert, the application of standard image

processing tools can yield varying results.

Army land managers need an alternate approach for correlating satellite imagery to ground-truth measurements. This

approach can be found in the application of neural networks.

This report presents the research results of using neural networks as a computational tool to correlate ground-truth data

with satellite imagery for Hohenfels, Germany, and to turn the imagery into maps of the installation.

14. SUBJECT TERMS 15. NUMBER OF PAGES

satellite imagery neural networks 56

Land Condition Trend Analysis (LCTA) 16. PRICE CODE

Geographic Resource Analysis Support System (GRASS)

17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT

OF REPORT OF THIS PAGE OF ABSTRACT

Unclassified Unclassified Unclassified SAR

ISN 7540-01-280-550 Standard Form 298 (Rev. 2-89)

Pre-ribed by ANSI Std 239-18

298-102
2 USACERL SR EC-94/28

Foreword

This report was funded by the U.S. Army Construction Engineering Research

Laboratories (USACERL) under the In-Laboratory Independent Research (ILIR)

Program.

Xiping Wu is a postdoctoral research associate from the Department of Civil

Engineering at the University of Illinois. Appreciation is expressed to Professor

J. Ghaboussi of the Department of Civil Engineering at the University of Illinois;

Susan Ribanski, Oak Ridge Affiliate Universities working at USACERL; and

Michael Shapiro, and David J. Tazik of USACERL, for discussions and help

provided during this study. The authors would also like to thank the following

Land Condition Trend Analysis (LCTA) personnel: Steve Warren, Pam Sydelko,

Julie Wentz, Cal Bagley, Bob Brozka, Paul Dubois, Lyle Trumball, and Jeff

Courson. Special appreciation is expressed to John Brent and Bernt Weber of the

Natural Resources staff at Hohenfels, Germany.

The work was monitored by the Environmental Compliance Modeling and Systems

Division (EC) of the Environmental Sustainment Laboratory (EL), U.S. Army

Construction Engineering Research Laboratories (USACERL). James D.

Westervelt, was the principal investigator. Dr. John T. Bandy is Chief, CECEREC, and Dr. William D. Goran is Chief, CECER-EL. The USACERL technical

editor was Gloria J. Wienke, Information Management Office.

LTC David J. Rehbein is Commander and Acting Director of USACERL, and Dr.

Michael J. O'Connor is Technical Director.
USACERL SR EC4 3

Contents

S F 29 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Forew o rd ............. ........................................... .. 2

List of Figures and Tables ............................................. 4

1 INTRO DUCTIO N ................................................. 7

Background .................................................... 7

O bjectives ..................................................... 8

A pproach ........................................... ........... 8

2 NEURAL NETWORKS ........................................... 10

Introduction .................................................... 10

Backpropagation Neural Networks ................................... 11

Adaptive Simulation of Multilayer Feedforward Neural Networks .............. 13

3 DEVELOPMENT OF CORRELATORS ................................ 17

A Neural Network-Based Approach .................................. 17

Development of Neural Network-Based Correlators ....................... 19

Comparison With the Theoretical Approach ............................ 21

4 CONCLUSIONS AND RECOMMENDATIONS .......................... 49

REFERENCES ..................................................... 51

DISTRIBUTION

AJtoeaion For

tDTIC 71,B

Urannu.... .

j" -

- --
4 USACERL SR EC-94/28

List of Figures and Tables

Figures

1 A Sample Backpropagation Neural Network ............................ 15

2 The Dynamic Node Creation Process ................................ 16

3 Architecture of the Neural Network for Percent Land Coverage Prediction ...... 24

4 Prediction Cf Percent of Land Cover (%LC) From the Neural Network

W ith 40 Training Data Sets ....................................... 25

5 Prediction of Percent of Land Cover (%LC) From the Neural Network

W ith 60 Training Data Sets ........................................ 26

6 Prediction of Percent of Land Cover (%LC) From the Neural Network

W ith 80 Training Data Sets ............................. .......... 27

7 The Map of Percent of Land Cover (%LC) Predicted by the Neural Network .... 28

8 Prediction of Percent of Bare Ground (%BG) From the Neural Network

W ith 40 Training Data Sets ........................................ 29

9 Prediction of Percent of Bare Ground (%BG) V rom the Neural Network

W ith 60 Training Data Sets ........................................ 30

10 Prediction of Percent of Bare Ground (%BG) From the Neural Network

W ith 80 Training Data Sets ........................................ 31

11 The Map of Percent of Bare Ground (%BG) Predicted by the Neural Network ... 32

12 Prediction of Percent Disturbance (%DIST) From the Neural Network

W ith 40 Training Data Sets ........................................ 33

13 Prediction of Percent Disturbance (%DIST) From the Neural Network

W ith 60 Training Data Sets ........................................ 34

14 Prediction of Percent Disturbance (%DIST) From the Neural Network

W ith 80 Training Data Sets ........................................ 35
USACERL SR EC-9428 5

15 The Map of Percent Disturbance (%DIST) Predicted From th,

Neural Network With 60 Training Data Sets ............................ 36

16 The Map of Forest Presence Predicted by the Neural Network .............. 37

17 Prediction of Percent Clay (%CLAY) From the Neural Network

W ith 40 Training Data Sets ........................................ 38

18 Prediction of Percent Clay (%CLAY) From the Neural Network

W ith 60 Training Data Sets ........................................ 39

19 Prediction of Percent Clay (%CLAY) From the Neural Network

W ith 80 Training Data Sets ........................................ 40

20 Prediction of Percent Sand (%SAND) From the Neural network

W ith 40 Training Data Sets ........................................ 41

21 Prediction of Percent Sand (%SAND) From the Neural Network

W ith 60 Training Data Sets ........................................ 42

22 Prediction of Percent Sand (%SAND) From the Neural Network

W ith 80 Training Data Sets ........................................ 43

23 Prediction of Percent Silt (%SILT) From the Neural Network

W ith 40 Training Data Sets ........................................ 44

24 Prediction of Percent Silt (%SILT) From the Neural Network

W ith 60 Training Data Sets ........................................ 45

25 Prediction of Percent Silt (%SILT) From the Neural Network

W ith 80 Training Data Sets ........................................ 46

26 The Map of Percent Clay Predicted by the Neural Network ................. 47

27 Comparison of Theoretical and Neural Network Results for Percent Land Cover . 48

Tables

1 The Training and Testing Results for Forest Classification ................. 21

2 Linear Regression Values for Comparing Model Predictability ............... 24
USACERL SR EC-94/28 7

1 INTRODUCTION

Background

To monitor the condition of natural resources on Army training and testing lands,

the U.S. Army Construction Engineering Research Laboratories (USACERL)

developed the Land Condition Trend Analysis (LCTA) program. LCTA is a

component of the overall Integrated Training Area Management (ITAM) program.

As part of the LCTA process, sample plots are scattered in a stratified random

fashion across training lands at an Army installation based on soil types and land

cover categories generated by an unsupervised classification of satellite imagery.

Sample plots are 100 by 6 meters (in) in size. Plant cover is determined by point

intercept along a 100-m transect that forms the longitudinal axis of the plot.

Woody plant density is determined by counting all individual plants in the plot or

subplot. Soil samples are collected, topographic features are recorded, a floristic

survey is conducted and the presence of wildlife is recorded. These methods are

documented in Tazik et al. (1992). Information regarding total plant cover,

percent bare ground, and percent disturbance is calculated for each plot. These

values are then correlated with satellite imagery in an attempt to extrapolate plot

data across the entire installation.

Current approaches to evaluating the condition of natural resources are

statistically based and attempt to generate rational mathematical formulae that

characterize the relationship between satellite imagery and transect values. These

approaches use the image data in conjunction with knowledge of the image access

time, satellite orientation, sun orientation, atmospheric conditions, time of year,

and weather conditions. Even in the hands of a well trained image processing

expert, the appropriate application of standard image processing tools can take

many forms, and yield different results.

An alternate approach for correlating imagery to ground-truth measurements can

be found in the field of neural networks. Research in neural networks, a paradigm

for computation and knowledge representation inspired by the neuronal

architecture and operation of the human brain, has experienced a considerable

resurgence of interest in recent years, although its foundation was laid in the

1940's. This renewed interest is supported by the realization that neural

computing is inherently parallel, and it has the capability of learning or self-
SI USACERL SR EC-94/28

organization. Moreover, new insight in the learning algorithms in recent years

has advanced the technology and raised the modeling power of neural networks

to a new level. With the advance and sophistication in some branches of neural

networks, this technology has been successfully applied to a number of fields,

especially in pattern recognition, image processing, functional modeling, and even

image classification with remotely sensed data.

The resurgence in research in neural networks has facilitated the development of

a decidedly different approach to correlating satellite imagery and ground-truth

data. With a neural network approach, the correlation can be captured within a

multilayer feedforward network through training the network with sampled

transect data. The neural network captures the relationship embedded in the data

in its weight structures, and no formal mathematical rules or formulae are

explicitly observable. As a computational entity, there is no technical barrier to

using this model within a graphic information system (GIS) such as the

Geographic Resources Analysis Support System (GRASS). This report presents

the research results in using neural networks as a computational tool to correlate

LCTA transect data with satellite imagery for Hohenfels, Germany, and then,

using the relationship in the form ci'a trained network within GRASS, to turn the

imagery into maps covering the entire installation.

Objectives

The main objectives of this research were to investigate the applicability of neural

network computing in constructing correlators for SPOT (Systeme Probatoire pour

r'Observation de la Terre) satellite imagery and LCTA data, to verify the approach

by comparing how well the network predicts land coverage to a theoretical

approach, and to study the implementation of neural network-based correlators

within GRASS.

Approach

Chapter 2 describes briefly the characteristics and operation of neural networks.

The learning procedures and algorithms of backpropagation neural networks, and

the adaptive simulation environment used in this study are presented in detail.

Chapter 3 addresses the neural network-based modeling methodology to correlate

satellite imagery and ground-truth data, the development of neural network-based

correlators for LCTA data, and a comparative study with a theoretical approach

to the correlation and prediction of percent land coverage condition. Chapter 4
USACERL SR EC-94/28

summarizes the results of this research and proposes further work in the

development of the neural network image process module within GRASS.
10 USACERL SR EC-94/28

2 NEURAL NETWORKS

Introduction

A neural network is a nonlinear dynamic system consisting of a large number of

highly inter-connected processing units, or processors. Each processing unit in the

network maintains only one piece of dynamic information (its current level of

activation) and is capable of only a few simple computations (adding inputs,

computing a new activation level, or performing threshold logical calculation). A

neural network performs "computation" by propagating changes in activation

between the processors; it stores the knowledge it has "learned" as strengths of the

connections between its processors. The large number of these processing units,

and the high inter-connectivity among them, similar to highly inter-connected

neurons in a brain, give the neural networks their capability of knowledge

representation. In addition, it is through self-organization or "learning" that a

neural network approaches some representation of a particular knowledge or

discovers the hidden relationships in data.

According to Rumelhart, Hinton, and Williams (1986), a neural network is

generally made up of the following components: (1) a set of processing units, (2)

the state of activation of a processing unit, (3) the function used to compute output

of a processing unit, (4) the pattern of connectivity among the processing units, (5)

the rule of activation propagation, (6) the activation function, and (7) the rule of

learning used. The network topology and the form of the rules and functions are

all learning variables in a neural network learning system, leading to a wide

variety of network types. Some of the well known types of neural networks are:

Competitive Learning (Grossberg 1976; Rumelhart and Zipser 1985), the

Boltzmann Machine (Hinton, Sejnowski, and Ackley 1984), the Hopfield Network

(Hopfield 1982), the Kohonen Network (Kohonen, Barna, and Chrisley 1988), the

Adaptive Resonance Theory (ART) (Carpenter and Grossberg 1987), and the

backpropagation neural networks (Rumelhart, Hinton, and Williams 1986).

Although many other variations of neural networks exist, the backpropagation

network and its variants, as a subset of multilayer feedforward networks, are

currently the most widely used networks in applications. The following

paragraphs describe the salient features and computational properties of

backpropagation networks.
USACERL SR EC-M926 1

Backpropagation Neural Networks

The backpropagation network is a multilayer feedforward neural network that

uses the generalized delta rule as its learning rule. The processing units in a

backpropagation neural network, which are similar to McColluch-Pitts neurons

with the exception that the output function or activation function is a sigmoidal

instead of a threshold step function, are arranged in layers. Each neural network

has an input layer, and output layer, and a number of hidden layers. Propagation

of activation takes place in a feedforward manner, from input layer to the output

layer. The pattern of connectivity and the number of processing units in each

layer may vary, with some constraints. No communication is permitted between

the processing units within a layer. The processing units in each layer may send

their output to the processing units in higher layers. The general architecture of

a backpropagation network is shown in Figure 1.

The learning process is determined by (1) the network architecture, in which

decisions are made on the number of layers, the size or number of nodes in each

layer, and the connection schemes between nodes in different layers, and (2) the

learning procedures, which include selecting the type of processing units, type of

activation function, and learning algorithms.

In a backpropagation network, two computational procedures are involved in a

learning cycle: feedforward computation of activations and backward propagation

of error signals for modifying connection weights via the generalized delta rule

derived by Rumelhart, Hinton, and Williams (1986). If you denote 2i, as the

strength or weight of connection between units i and j, a feedforward computation

proceeds as follows:

1. The units in the input layer receive their activations in the form of an

input pattern and this initiates the feedforward process.

2. The processing units in each layer receive outputs from other units and

perform the following computations.

a. Compute their net input NJ,

M

Nj = E Wjk Ok [Eql]

k=1

Figures are located at the end of each chapter.
12 USACERL SR EC-94/28

where: ok = output from units impinging on unit j, and

M = number of units impinging on unit j.

b. Compute their activation values from their net input values,

a, = Fj(Nj) [Eq 2]

where F, is usually a sigmoid function.

c. Compute their outputs from their activation values. In the neural

network type used in this study, the output is the same as the

activation value.

= a [Eq 3]

3. The output values are sent to other processing units along the outgoing

connections.

4. This process continues until the processing units in the output layer

compute their activation values. These activation values are the output

of the neural computations.

The generalized delta rule is basically a steepest descent scheme with constant

step length in a network setting, performing a gradient descent on the error

function with respect to the weight space. For multilayer feedforward neural

networks, the error function is usually a highly nonlinear function defined as:

E(w ) -- r1M 1 N

N( [NL,, 41

k-1

where Ek = I t(xk - O(Xk, w) 12, t(x•) is the expected output; o(xk, w) is the network

prediction, which is a function of the input vector x and the network weight vector

w, and N is the number of total training cases. Therefore, the modification of the

strengths or weights on the connections with the generalized delta rule is

accomplished with the following formulas:

AwU = n vE(w 1,) = n 65 01 [Eq 5]

In this equation, - is a learning constant called the "learning rate" and 6, is the

gradient of the total error with respect to the net input at unit j. At the output

units, 5, is determined from the difference between the expected activation t, and

the computed activation a,:
USACERL SR EC-94/28 13

(t- aj) F'(N) [Eq 61

where F' is the derivative of the activation function. At the hidden units, the

expected activations are not known a priori. The following equation calculates 5,

for the hidden units:

M

E( 6 kWjk) F'(N ) jEq 71

k- 1

Due to their popularity in applications, multilayer feedforward neural networks

have been extensively studied, and their general mapping capability has been well

understood. Hecht-Nielsen (1987) uses Kolmogorov's superposition theorem to

show the general functional modeling capability of a multilayer feedforward

network; Gallant and White (1988) prove that a three-layer network with one

hidden layer is capable of embedding a Fourier analyzer and recently Hornik,

Stinchcomebe, and White (1989) prove that multilayer feedforward networks are

universal approximators. The complexity of learning and the mathematical theory

of generalization have also been studied in depth by some other researchers (Judd

1990; Wolpert 1990).

Adaptive Simulation of Multilayer Feedforward Neural Networks

The generalized delta rule performs a search that results in a very slow

convergence rate in training.* On the other hand, for a backpropagation network,

the network architecture of the hidden layers cannot be optimally determined a

priori. To solve real world problems efficiently, it is imperative to construct a

simulator with a fast learning scheme and some ways to determine the network

architecture as the training proceeds. To date, extensive research has produced

many new learning algorithms with improved learning rates by using either

higher order information or heuristic rules (Falhman 1988; Jacobs 1988; Watrous

1987), and mechanisms for dynamic architecture generation and evaluation (Ash

1989; Tenorio and Lee 1989; Karnin 1990).

To efficiently determine the network architecture and training process, an

adaptive simulator called DQP (Wu 1991) that implements the quickprop

algorithm (Falhman 1988) and a variant of the Dynamic Node Creation scheme

The search is a gradient descent search in the weight space through minimization of a mean-squared error

lunction.
14 USACERL SR EC-94/8

(Ash 1989), is used in this study. The implemented learning algorithm and hidden

nodes creation scheme are briefly described in the following paragraphs.

With heuristic reasoning, Rumelhart, Hinton, and Williams (1986) introduced a

momentum factor to the generalize delta rule so that the formula for weight

update becomes:

Aw(t) = -n aE/aw(t) + a Aw(t- 1) [Eqa8

where rj is the learning rate and a is the momentum factor, and both of them are

assumed constants. In spite of its simplicity, this modification usually results in

some improvement on the robustness of the learning performance over the purely

gradient descent-based scheme. To adaptively estimate the momentum factor, a,

a learning algorithm called quickprop was proposed by Falhman (1988), in which

the formula for weight update becomes:

Aw(t) = - r aEI/aw(t) + aE/aw(t) Aw(t- 1) [Eq 9]

aEldw(t- 1)- aElaw(t)

Numerical experiment indicates that the quickprop algorithm is robust in

learning, about an order faster than the generalized delta rule in terms of training

epochs, and the scheme seems scaled-up well for large problems.

Based on the theoretical conclusion that a three-layer feedforward network is a

universal approximator (Hornik, Stinchcomebe, and White 1989), Ash developed

the Dynamic Node Creation scheme within backpropagation networks, by fixing

the network architecture to three layers with one hidden layer, starting training

with one hidden node, and adding one hidden node at a time during a certain

training period until the convergence of learning is realized. When a hidden node

is added to the hidden layer, connections from this node to all the other input and

output nodes are created, and the connection weights initialized. The criterion for

adding a new hidden node is governed by the condition that the currently

estimated average error slope over a certain number of epochs be less than a

predefined gradient tolerance called the "trigger slope." Though this scheme

appears to be robust and slightly faster than a standard backpropagation

algorithm for training the encode/decode problems, the proper selection of the

trigger slope plays an important role in the ultimate performance. However, this
USACERL SR EC-94/28 15

scheme is simple to use and usually results in a quasi-optimal architecture. The

dynamic node generation process is schematically illustrated in Figure 2.

With the above observation, a heuristic node-adding rule is developed in this study

for the preliminary stage of training, in which the trigger parameter is defined as

a percentage of correct predictions over the total training cases. After the network

has settled in the solution space (about 80 percent correct prediction), the

architecture adjusting is manually controlled. The training process converges

when both the maximum absolute error and the total error are below their

respective tolerances.

The performance of DQP has been investigated on a large set of benchmark

problems and modeling of some chaotic time series predictions (Wu 1991). The

compound approach appears to be efficient and robust in modeling real value

functions. It not only has a faster learning convergence rate than the generalized

delta rule, but also determines the network architecture as the training

progresses.

Hidden

In1put Output

Layer Layer

Figure 1. A Sample Backpropagation Neural Network.
16 USACERI SR EC-94/28

Input---- Otu

Layer Layer

(a) The initial Architecture of a Neural Network

new connections

Inp ut Output

Layer j idnLayer

Layer

(b)1'Flie Current Architecture after Adding One hfidden Node

Figure 2. The Dynamic Node Creation Process.
USACERL SR EC-94/28 17

3 DEVELOPMENT OF CORRELATORS

A Neural Network-Based Approach

The basic strategy for developing a neural network-based correlator that captures

the relationship between satellite imagery and ground-truth data is to train an

error backpropagation type of neural network on the transect values derived from

ground-truth data and the raw SPOT satellite images corresponding to these

transects. If a relationship between the satellite imagery and transect values from

ground-truth data exists, and a comprehensive set of data that characterize the

relationship is available, then the trained neural network should contain sufficient

information about the embedded correlation. Such a trained network would not

only be able to reproduce the training data set with reasonable accuracy, but

through generalization it should be able to approximate the transect values

directly from satellite imagery that were not processed through the network. The

degree of accuracy in this generalization depends on how comprehensive and

representative the training data set is.

The training of a backpropagation neural network with appropriate data

containing the information of transect values and corresponding satellite imagery

is at the heart of this research. A correlator is established after the training is

completed and the trained network is appropriately tested on some new sets of

data. The data for this study comes from a ground survey and SPOT satellite

image that covers the Hohenfels Combat Maneuver Training Center, Germany.

The ground-truth data was collected by a team of iesearchers during August 1988.

The SPOT image is dated August 1988 also. Ground-truth data was painstakingly

collected on 90 100 x 6 meter transects randomly situated in the study area

The ground-truth data was processed by the data collection team to provide a

whole spectrum of transect values including the percent of the bare ground shaded

by vegetation across the entire transect (LC), the percent disturbances (DIST), the

percent bare ground (BG), the presence of forest (FOR), the percent clay (CLAY),

the percent sand (SAND), and the percent silt (SILT). For each transect value, the

corresponding band intensity values at each transect need to be determined. To

accomplish this, the satellite image was geographically referenced to a basemap

of the region. Each transect was overlaid on this map to obtain the pixel intensity

values contained in the transect. Proportional amounts of each pixel were
1i USACERL SR EC-94/2

combined to generate a single representative band intensity value for each

transect. At this stage we have, for each transect, a real valu, ;uch as the percent

land cover value and three intensity values representing the three SPOT bands.

Thus, the objective is to train the network with the three-band image data 'nd

with the corresponding transect value on a backpropagation neural network to

capture the correlation between them.

Decisions regarding the neural network architecture and learning algorithms are

normally made before training and testing begin. The composition of the input

and output layers is solely determined by the problem and representation scheme

used. Hence, three input nodes representing the three SPOT bands and one

output node representing the transect value (e.g., percent land cover) are used.

With the aid of recent theoretical studies on the mapping capability of neural

networks (Hornik, Stinchcomebe, and White 1989), it was determined that one

hidden layer should be used for this research; the size of the hidden layer was

determined with the dynamic node creation scheme during training.

Because of the use of the sigmoidal function in the activation calculation, the raw

image data and transect values train most efficiently when normalized into the

range of 0.10 through 0.90 before they are presented to the network for training

and testing. The normalization scheme can be linear or nonlinear; the simple

linear scaling schemes used for this study are:

1. Three channels of image data: new_value = old value/100.0;

2. Percent land cover: new-value = 0.10 + 0.80 x old_value;

3. Percent of forest: new_value = 0.10 + 0.80 x oldvalue;

4. Percent disturbance: newvalue = 0.10 + 0.80 x old_value;

5. Percent bare ground: newvalue = 0.10 + 0.80 x oldvalue;

6. Percent sand in soil: newvalue = 0.10 + old_value; and

7. Percent silt and clay: no scaling.

After preparing the entire data set with the scaling schemes, a sampling procedure

selects the training data set randomly from the 89" sets of data with three sample

sizes: 40, 60, and 80. Four replications were done for each sample size. With this

approach, the effect of sampling on the training and testing performance of the

neural networks can be qualitatively estimated by performing statistical analysis

on the results.

One of the initial 90 sets of transect data was incomplete, reducing the usable data to 89 transects.
USACERL SR EC-94W28 19

When the training and testing on the 89 sets of transect data were completed, the

trained neural network that captured the correlation between the satellite imagery

and a transect value was then incorporated into GRASS to generate the land

coverage condition maps for the entire training area directly from the satellite

imagery.

The following section describes the training, testing, and architecture evolution in

the development of each neural network-based correlator in detail.

Development of Neural Network-Based Correlators

Procedures used in the development of neural network-based correlators for

percent land cover, percent bare ground, percent disturbance, identification of

forest, and percent silt, clay, and sand, are essentially the same. Construction of

the correlator for satellite imagery and percent land cover (LC) are described in

detail in the following paragraphs as an example.

Percent Land Cover (LC)

The network architecture for correlating the satellite imagery with land cover

condition consists of three layers, as shown in Figure 3. The three input nodes

represent the three SPOT bands and the one output node represents the percent

land cover value. The size of the hidden layer is determined by the dynamic node

creation scheme, and varies with the size of the training data set. The hidden

layer was started with 10 nodes; the final hidden size was determined to be 18

after the network trained on the entire transect data set (89 sets of data).

To determine the existence of a relationship between the imagery data and percent

land coverage value and the generalization capability of the neural network, the

network was trained with data sets consisting of randomly selected samples of 40,

60, and 80 transects out of the total 89 sets of data, and tested with the remaining

samples in each size category. Four replications were done for each sample size.

The training and testing results for a typical sample in each size category, and the

corresponding correlation coefficients, are shown in Figure 4. The training results

are plotted as triangles and the testing results as filled circles. Graphs (a)

through (c) show the training and testing results for a sample size in each of the

three size categories for training data, and graph (d) shows the overall performance of the trained neural network in terms of the correlation (R2 value) between

the predicted value and the expected value. The correlations for all the samples

are in the range of 0.894 through 0.931. This indicates that a strong relationship

exists between the satellite imagery and percent land cover in the transect data
20 USACERL SR EC-94M20

set, which in turn shows that the embedded relationship has been reasonably

captured by the neural network after proper training.

The training and testing results for all the four replications in each size category

are shown in Figures 4 through 6. In these figures, the expected percent land

cover from the original LCTA data set is plotted against that predicted from the

neural network, and the correlation for each sampling case is also included. It is

obvious that sampling does make some difference in the learning and testing

performance of the neural network. However, the effect of sampling is statistically

insignificant because the correlation for each sampling run is essentially in the

same range (about 0.90). The minor improvement on the correlation with the

increase in the size of the training sample indicates that a strong correlation

exists between the LCTA data and the imagery, and the correlation is well

captured in the neural network model.

To determine the generalization capability of the neural network after training all

the transect data sets, the trained neural network shown in Figure 3 was

incorporated into GRASS to generate a percent land cover map for the Army

training installation in Bavaria, Germany. The prediction of the neural network

correlator on the entire image is shown in Figure 7. The percent land cover

predicted is acceptably accurate.

Percent Bare Ground (BG)

The network architecture for correlating the percent bare ground with SPOT

satellite imagery is similar to the one shown in Figure 3. As with the training of

percent land cover data, the number of hidden nodes increases as the sample size

changes from 40 to 80 sample data sets, and the final number of hidden nodes

reaches 18 when the 80-sample data set is trained. The overall performance of the

neural networks is illustrated in Figures 8 through 10, where the correlations (R2

values) between the expected values and network predictions on training different

sized samples of data are plotted. The training and testing results for training the

four replications of sample sizes of 40, 60, and 80 are also shown. The map shows

the percent of bare ground from the neural network correlator constructed from

training all the 89 sets of data on the entire image as shown in Figure 11. The

prediction appears reasonable.

Percent Disturbance (DIST)

With a three-layer network, the correlator for predicting percent disturbance was

built with 22 nodes in the hidden layer after the 80 sample data set was trained.

The training and testing results for the four replications of sample sizes of 40, 60,
USACERL SR EC-94'28 21

and 80 are shown in Figures 12 through 14. The map in Figure 15 shows the

percent disturbance of the training land from the neural network correlator after

training all the data sets.

Forest Classification (FOR)

The correlation and identification of the presence of forest from the imagery and

ground-truth data is basically a classification problem. With a three-layer

network, the hidden nodes were determined to be 12 after training 80 sets of

sample data. The network learned the correlation almost perfectly and the map

generated from this correlator predicts very well the presence of forest in the

training land, which is shown in Figure 16. Table 1 lists the training and testing

results on training four replications of 40, 60, and 80 samples.

Percent Clay (CLAY), Percent Sand (SAND), and Percent Silt (SILT)

The construction of correlators between the SPOT satellite imagery and

percent clay, sand, and silt in the soil was completed in the same manner as the

other correlators. All the three networks with one hidden layer required 18

hidden nodes to learn the correlation to some degree. The training and testing

results for percent clay are shown in Figures 17 through 19, for percent sand in

Figures 20 through 22, and for silt in Figures 23 through 25. The map that shows

the distribution of clay in the training land generated from the network is shown

in Figure 26.

Comparison With the Theoretical Approach

The neural network-based approach to correlating satellite imagery and groundtruth data is fundamentally different from the traditional approach. Instead of

using human idealization and simplification on the data, the neural network-based

Table 1

The Training and Testing Results for Forest Classification

Sample Percent of Correct Predictions (%)

Size Training Results Testing Results

40 100 97.5 100 100 95.9 98.0 95.9 95.9

60 100 100 98.3 100 96.5 100 96.6 96.6

80 98.8 95.0 97.5 93.8 88.9 100 100 100
22 USACERL SR EC-94/28

correlator was developed through training on the raw transect data and the

corresponding satellite imagery. To illustrate the difference between these two

approaches and to further verify the validity of the neural network-based

correlator, a comparative study was performed using a more traditional approach

based on more standard image processing tools on the same data set that had been

used for constructing the neural network-based model.

Traditional image processing steps have strong theoretical basis for image

manipulation. Spectral vegetation indices (SVI), bidirectional reflectance

distribution function (BRDF), and leaf area indices (LAI) are but several

theoretically-based operations to help extract vegetation information from images

(Jensen 1986; Lillesand and Kiefer 1987). Similarly, other operations are defined

for dealing with atmospheric conditions and corrections required duc to land

topography. Such operations still leave one with processed images containing a

tremendous variety of pixel band intensity values, which often are clustered into

several categories. These categories are then labeled to suggest a land-cover

characteristic that exists. The multidimensional boundaries that separate the

clusters are generally arbitrary and more a function of the number of classes the

operator chooses rather than an identification of any real boundaries inherent in

the image data. Clearly, successful image processing using standard image

manipulations techniques is time consuming and expensive. As inexpensive image

processing tools become more accessible to people not trained in traditional image

processing techniques, the need for simple-to-use processes that return useful

results becomes increasingly urgent.

In the traditional approach, the first step is to adjust the data for each of the three

SPOT satellite bands to compensate for solar irradiance, sensor spectral band

location, band width, and varying zenith angle of the sun (Price 1987). This

converts the raw sensor values to a common scale that represents surface

reflectance. This operation can be performed with the following equation:

n L d2

RK - [Eq 101

ESUN k cosO,

where k = image band

R = unitless effective at-satellite reflectance

L = pectral radiance defined as W/(m2srpam), in which

W = watts

sr = solar, and

pm = micrometers in light band
USACERL SR EC-94/28 23

d = Earth to Sun distance in Astronomical Units

ESUNk = mean solar exoacmospheric irradiance defined as W/(m2pn)

E). = solar zenith angle, and

3.1415926. S= 

The next step is to adjust these computed reflectance values to compensate for

atmospheric conditions. Because actual atmospheric conditions were not explicitly

known, this involved calibrating the images using pixels that have a known actual

reflectance; in this case, pixels that represented a runway. The smallest values

in each of the three bands found across a series of images of the same location are

assumed to be most correct. All image bands then get proportionally adjusted to

compensate. This procedure is based on Lo, Scarpace, and Lillesand (1986).

The third step is to calculate the normalized difference vegetation index (NDVI).

This index is likely to have a strong correlation with the percent land cover values

measured in the ground-truth procedure. The calculation involves subtracting the

red band value from the near infrared value and dividing the result b3 the sum

of these two values.

Finally, the NDVI values computed for each transect were regressed against the

measured percent land cover values. Using all 89 transects, only a very poor

correlation was discovered. After hypothesizing that the NDVI works best in

grasslands, the regression was performed only for transects that did not contain

forested areas. The resulting equations were applied to every pixel in the entire

image to generate images showing derived percent land cover.

The neural network approach to correlating SPOT satellite imagery with percent

land cover data collected on random transects compares very favorably with an

approach based in image processing theory. Figure 27 shows measured percent

land cover data (plotted on the x axis) versus percent land cover data modeled

with the theory-based approach (using all transects) in graph a, using only nonforested transects in graph b, and the neural network in graph c. Compare graphs

a and c with graph b and notice that graph b, the neural net modeled data, is

more compact. Table 2 provides a comparison of the regressions of the measured

data and several model inputs and outputs. Bands 1 through 3 represent the raw

band data; note that there is ", -ne degree of natural correlation between the

measured data and the raA satellite imagery. The theoretical-based models

perform well. Figure 27 (d) shows that the neural network provides a network

nearly identical to that generated based on theory; the last entry in Table 2

(R2=.9955) further demonstrates this fact.
24 USACERL SR EC-04/26

Table 2

Linear Regression Values for Comparing Model Predictability

Regression Between R2

Image band 1 - LCTA measured %Iand cover 0.7954

Image band 2 - LCTA measured %land cover 0.6546

Image band 3 - LCTA measured %land cover 0.8585

Theoretical model with forest - LCTA measured %land cover 0.9652

Theoretical model w/o forest - LCTA measured %land cover 0.9686

Neural network model - LCTA measured %land cover 0.9851

Neural network model - Theoretical model w/tforests %land cover 0.9955

Note: The neural network model trained the whole 90 data sets.

In another aspect, not only does the neural network emulate the theoreticallybased model, but it also proves adaptable for simultaneously modeling the forested

lands. Note that the forest data [represented by the data furthest to the right in

Figures 27 (a) and (c)0 falls closer to the regression line in the neural net model

[Figure 27 (b)].

Hidden Layer:

18 nodes

Layer Otu

Band I "--- Layer

Band 2 Percent land

cover

Band 3

Figure 3. Architecture of the Neural Network for Percent Land Coverage Prediction.
USACERL SR EC-94/28 25

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Figure 4. Prediction of Percent of Land Cover (%LC) From the Neural Network With 40

Training Data Sets.
26 USACERL SR ECMU/M1

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Figure 5. Prediction of Percent of Land Cover (%LC) From the Neural Network With 60

Training Data Sets.
USACERL SR EC-94/28 27

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Figure 6. Prediction of Percent of Land Cover (%LC) From the Neural Network With 80

Training Data Sets.
FUSACERL SR 4M

Figure 7. The Map of Percent of Land Cover (%LC) Predicted by the Neural Network.
USACERL SR EC-94/28 29

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Figure 8. Prediction of Percent of Bare Ground (%BG) From the Neural Network With 40

Training Data Sets.
30 USACERL SR EC-94/28

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Figure 9. Prediction of Percent of Bare Ground (%BG) From the Neural Network With 60

Training Data Sets.
USACERL SR EC-94128 31

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Figure 10. Prediction of Percent of Bar. Ground (%BG) From the Neural Network With 80

Training Data Sets.
U USAiERL SR EC44/

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Figure 11. The Map of Percent of Bare Ground (%BG) Predicted by the Neural Network.
USACERL SR EC-94/28 33

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Figure 12. Prediction of Percent Disturbance (%DIST) From the Neural Network With 40

Training Data Sets.
34 USACERL SR E-C4•M

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Figure 13. Prediction of Percent Disturbance (%DIST) From the Neural Network With 60

Training Data Sets.
USACERL SR EC-4/28 35

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Figure 14. Prediction of Percent Disturbance (%DIST) From the Neural Network With s0

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.36USACEAL SR EC44'2

Figure 15. The Map of Percent Disturbance (%DIST) Predicted From the Neural Network

WVIth 60 Training Data Sets.
USACERL SR EC-94/28 37

Figure 16. The Map of Forest Presence Predicted by the Neural Network.
38 USACERL SR EC-94/28

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USACERL SR EZ-94/28 39

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Figure 18. Prediction of Percent Clay (%CLAY) From the Neural Network With 60 Training

Data Sets.
40 USACERL SR EC-94/28

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Expected Expected

Figure 19. Prediction of Percent Clay (ACLAY) From the Neural Network With 80 Training

Data Sets.
USACERL SR EC-94/26 41

0.6 0.6

ST r a in e d : 4 0 s o o f d a t a

Trained: 40 sets of data 40 sets of data Trained: 

A Trol05 Tm~e~g ecit os A Training results results 0.5 a Testing results 0-5 a Testing results

0.4 A 0.A

Oz 0 . 3M 6 . 44 A * a g A.A A A 0.3 U A h * o. . oA

A A EA 0. E 0.2 * A

0. AA A

0.1 0.A

0.0 0.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Expected Expected

0.6 . I I 0.6 

Trolne 40 sets • of data Trained: 40 "to of data

A Training results A Training results

0.5 0 Testing results 0.5 e Testing results

S0.4 UA - 0.4

A Z S A

f O0. 1 0.3

A0. oC A 0A A. AA 

- AA A 

0.1 0.1

AA

0.0 * * *I* 0.0 * * * *

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Expected Expected

Figure 20. Prediction of Percent Sand (%SAND) From the Neural network With 40 Training

Data Sets.
42 USACERL SR EC-94/2U

0. . 0.0

Trained: 60 "to of data Trained: 60 sets of data

A Training results A Training resulte

0.5 a Teoting resume 0.5 0 Teuting resulta

0.4 0.4

AA A A

z AL

0.2 A o2 A # A A

0.1 A # 0.1 A

0.0 0.0

0.0 0.1 0.2 0.3 0.4 0.3 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Expected Expected

0.6 . - .0.61 1 1 1 .

Trained: 60 sets of data - Trained: W0 sets of data

A Training resulta A Training results

0. Tesining rsls0.5 a Tooting results

"1 0.4 UI 0.4

A

0.23 A 0.2 AA AA

I *A AA At Ao A

z A a AA 

0.1 / 0.1 A

0.0 , I , I . I . 0.0 , I * I , I * I

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Expcted Expected

Figure 21. Prediction of Percent Sand (%SAND) From the Neural Network With 60 Training

Data Sets.
USACERL SR EC-94/26 43

0.6 1 1` 1 I I I , . . . ,0.6

Trained: S0 get of data Traned: 80 sets of data

A Traning rsuft A Trainng results

0.5 6 Tesig reuits 0.5 S Testing results

0.4 0.4

0.3 LA 0.5 0.J 0.3 A G.5 &

AL LA te Ap A tA

!o. 4A A A& A& IAA

At L A

&~AL

0.1 AA0.1 A A

0.0 0.0

0.0 * , i . p _______________0____0_

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Expected Expected

Trane22. 80 sets of d Pta TrainedD 80 sets of data a Traing relts A Trelng results 0.5 6 Tgestng result. 0.5 a Testing results

0.4 1 0.4

03A03AA A a.

UA6 a A A 'LA A A

A A A A A 03 AA

0 L.2 A 0.2 A A A

AA

0.1 0.1 A

A A

0.0 I 0. , , I~ ,

0.0 0.1 0.2 0.3 0.4 0M5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Ex1 Ae Expected

Figure 2Z~ Prediton of Percent Sand (%AND) From the Neural Network With 80 Training

Data Soft.
44 USACERL SR EC44/28

0.8 • 0.8 • I .I .

Trained: 40 sets of data Trained: 40 sete of data

A Training results A Training results

0.7 a Testing results 0.7 a Testing reeults C/

0 0 - 0.6 S0.6 

a A a AD A 0

00.5if A A 0o.5 -C. AA A-. 

0.4 A 1 A A A 0.4 ga& A *00so;

z

0.3 0.3

0.2 0.2 0 . . . ..

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Expected Expected

0.8 / . , . , . , . . . o0.8 .

Trained: 40 sets of data Trained: 40 sets of data

A Training results A Training results

0.7 8 Testing rsult 0.7 9 Testing rul-ets

.2

0. oA 0.5 A

?t Ad A A

O0.5 0.3A A 0.5 08.A a 7 0

111A As A

A4 z A.

-~0.4 AAA'AA A E 0.4 a0

Expected Expected

Figure 23. Prediction of Percent Silt (%/SILT) From the Neural Network With 40 Training Date Sets.
USACERL SR EC-94/28 45

0. . I V I I .". I I I I 

Trained: 60 reas of data Trained: 60 sets of data

A Training results & Training results

0.7 * Testing results 0.7 a Testing results

C£

o.06 A o 0 .6 - S"

0o.5 A A 0.5A

""O & @A S A A£

a A

Z £ A A

0.3 0.3

0.2 0.2 , ' I * I , I ,

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Expected Expected

Trained: 60 sets of data Trained: 80 "ets of dota

A Training results A Training results

0.7 a Testing results 0.7 * Testing results

0

0 Aa

- 0 .6 -0 . 5 A A A A

*A a6

0.5 Vg~ IA 0.5 % AA

A A A •

0.4 A A A 0.4 A A

z

0.3 0.3

0.2 ,0.2 1 , L , ,

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Expected Expectd

Figure 24. Prediction of Percent Silt (%SILT) From the Neural Network With 60 Training

Data Sets.
46 USACERL SR EC-94'1

0.8 . 0.8

Trained: 80 sets of data Trained: 80 sets of data "A Training results A Training results

0.7 a TesUng remsuit 0.7 - Testing results

A AA

A A && A A*

4&I A AA 

A A A A " A

A •.. Aa A A& ,• A,

0 0.4 A 0.4 .AA A A

A A A 

A

0.3 0.3

0.2 0.2 .1 *I* *

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Expected Expected

0.5 --1- 1'= - 'I 'V 0.8 1 1 . I

Trained: 80 sets of data Trained: 80 sots of data

A Training results A Training results 0.7 0 Teeting results 0.7 S Testing results

.9 A

0.6 A A A A ,0.2 AA AA a

*A £60 A

2 L A LA AL 0.LA %A# A

0.5 A A A 0.5 A 0A 

0.&A A tats

AAA a A

AA

A

0.3 0.3

0.2 0.2 L

0.2 0.3 0.4 0.5 0.8 0.7 0.8 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Expected Expected

Figure 25. Prediction of Percent Slit (%SILT) From the Neural network With 80 Training

Data Sets.
USACERL SR EC-94/28 47

Figure 26. The Map of Percent Clay Predicted by the Neural Network.
48 USACERL SR EC-94/28

1.0 1 I 1.0 1

TheorwUcol Model w/ Foret A Neural Network Model 05

(AA . A A A (a) A L 0.8 ()A A

A A A A A

0.6 A A 0.6 AA

A AL A A

0.0 # A 0.4 A A

A A

0.2 0.42

AA

ALA

0.0 A0.0

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

Expected Expected

1.0 1.0 - Theoretical Model w/o Forest A Theoretical vs. Neural Net Model A

CA (C) ALAA (d) A

0.0 A A O I i

A

A

0.6 A A0A2

0.0 0.0

0.0 U.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.X 0.8 1.0

Expected Neural Net Model

Figure 27. Comparison of Theoretical and Neural Network Results for Percent Land Cover.
USACERL SR EC-94/28 49

4 CONCLUSIONS AND

RECOMMENDATIONS

The neural network is very applicable for correlating satellite imagery and groundtruth data and performs better than the theoretical approach in severda areas.

Most important is that the operator's level of expertise in pursuing the neural

network approach is far less than that required by the theoretical approach. This

work was motivated by requirements to support such a user community; a group

of trained scientists (ecologists, land managers, and foresters) working in an

operational rather than academic environment.

A neural network-based GIS program can be used to attempt correlations between

imagery data and other measured land cover information. The correlators

constructed for predicting percent bare ground and percent ground disturbance

appear to be reasonable and have reasonable statistical measurement. However,

it is obvious that the training and testing results of correlating information on the

percent clay, sand, and silt were not satisfactory, which indicates that additional

information other than the three bands of imagery is needed to fully capture the

relationships for these transects. More likely is that imagery is unsuitable for soil

particle analysis, particularly when plant cover and/or organic matter content is

high.

From the results obtained in this initial stage of research, it can be expected that

a neural network-based approach will be very effective, not only in the correlation

of satellite imagery and ground-truth data, but also in solving certain suitable

classification and estimation problems encountered in land planning and

management. However, further research on both the theory and application of

neural computing needs to be carried out to make this approach a practical and

efficient modeling procedure.

The goal of future research should be to capture the neural network software into

a GIS program in which the user will need to identify only the input imagery

bands, a file containing the percent land cover data, and, optionally, a few neural

network parameters. Currently, the training and testing of the neural network

modeling process still require the modeler to have a fairly good knowledge of the

theory of neural computing; a fully adaptive modeling process is yet to be realized.

Extended research in the development of adaptive training algorithms, therefore,
s USACERL SR EC-4/

becomes imperative to reduce the burden in training and determining the network

architecture.

To take advantage of the sophisticated image process capability in GRASS, it is

important to incorporate the neural network modeling tools within GRASS to form

an integral package.

To construct a robust and reliable neural network-based correlator, training and

testing with the ground-truth data and corresponding satellite imagery collected

over a long period of time and during different seasons should be studied.
USACERL SR EC-94/28 51

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