the use of digital image processing to facilitate digitizing land cover zones from gray level aerial

119 Pages·2012·4.06 MB·English

THE USE OF DIGITAL IMAGE PROCESSING TO FACILITATE DIGITIZING
LAND COVER ZONES FROM GRAY LEVEL AERIAL PHOTOS

A THESIS PRESENTED TO
THE DEPARTMENT OF GEOLOGY AND GEOGRAPHY
IN CANDIDACY FOR THE DEGREE OF
MASTER OF SCIENCE

By
JOAN M. BIEDIGER

NORTHWEST MISSOURI STATE UNIVERSITY
MARYVILLE, MISSOURI
April 2012
DIGITAL IMAGE PROCESSING

The Use of Digital Image Processing to Facilitate Digitizing
Land Cover Zones from Gray Level Aerial Photos
Joan Biediger
Northwest Missouri State University

THESIS APPROVED

____________________________
Thesis Advisor, Dr. Ming-Chih Hung Date

____________________________
Dr. Yi-Hwa Wu Date

____________________________
Dr. Patricia Drews Date

____________________________
Dean of Graduate School, Dr. Gregory Haddock Date
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The Use of Digital Image Processing to Facilitate Digitizing

Land Cover Zones from Gray Level Aerial Photos

Abstract

Aerial imagery from the 1930s to the early 1990s was predominantly acquired using
black and white film. Its use in remote sensing applications and GIS analysis is
constrained by its limited spectral information and high spatial resolution. As a historical
record and to study long-term land use/land cover change this imagery is a valuable but
often underutilized resource. Traditional classification of gray level aerial photos has
primarily relied on visual interpretation and digitizing to obtain land cover classifications
that can be used in a GIS. This is a time consuming and labor intensive process that can
often limit the scale of analysis.
This research focused on the use of digital image processing to facilitate visual
interpretation and heads up digitizing of gray level imagery. Existing remote sensing
software packages have limited functionalities with respect to classifying black and white
aerial photos. Traditional image classification alone provides limited results when
determining land cover types derived from gray level imagery. This research examined
approaching classification as a system which uses digital image processing techniques
such as filtering, texture analysis and principle components analysis to improve
supervised and unsupervised classification algorithms to provide a base for digitizing
land cover types in a GIS. Post processing operations included smoothing the
classification result and converting it to a vector layer that can be further refined in a GIS.
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Software tools were developed using ArcObjects to aid the process of refining the vector
classification. These tools improve the usability and accuracy of the digital image
processing results that help facilitate the visual interpretation and digitizing process to
gain a usable land use/land cover classification from gray level imagery.

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TABLE OF CONTENTS

ABSTRACT…………………………………………………………………………. iii
LIST OF FIGURES……………………….………………………………………….vii
LIST OF TABLES………………………………………………….………...….…. viii
ACKNOWLEDGMENTS……………….…………………………………..………..ix

CHAPTER 1: INTRODUCTION……………………….……………………………1
1.1 Research Objective…………………………………………………………… 4

CHAPTER 2: LITERATURE REVIEW…...……………………………………….. 5
2.1 Historical Aerial Imagery Uses and Importance……………………………... 5
2.2 Classification Problems of High Resolution Panchromatic Imagery………….6
2.3 Statistical Texture Indicators…………………………………………………. 9
2.4 Image Enhancements and Filtering……………………………………….….. 13
2.5 Image Segmentation and Object-based Image Analysis……………………... 15

CHAPTER 3: CONCEPTUAL FRAMEWORK AND METHODOLOGY……... ….17
3.1 Description of Study Area………………………………………………….… 17
3.2 Description of Data…………………………………………………………… 17
3.3 Methodology………………………………………………………………….. 21
3.3.1 Conceptual Overview…………………………………..……………….. 21
3.3.2 Software Utilized…………………………………………………………22
3.3.3 Preliminary Image Processes……………………………...………….…. 24
3.3.4 Unsupervised Classification.……………………….…………………… 27
3.3.5 Supervised Classification……………………………………………….. 33
3.3.6 Image Enhancement and Texture Analysis……………………....………35
3.3.7 Object-based Image Analysis………………….…………………………38
3.3.8 Post Processing and Automation…………………………………………40
3.3.9 Accuracy Assessment………………………………………...……….….43

CHAPTER 4: ANALYSIS RESULTS AND DISCUSSION……………….………46
4.1 Manual Digitizing…………………………………………………………….. 46
4.2 Unsupervised Classification………………………………………………….. 48
4.3 Supervised Classification…………………………………………………….. 53
4.4 Image Enhancements and Texture Analysis……….………..………………... 57
4.5 Object-based Image Analysis………………………………………………… 63
4.6 Post Processing and Automation……………………………………………... 65
4.7 Classification Accuracy and Results…………………………………………. 70

CHAPTER 5: CONCLUSION………………………………………………………81
5.1 Limitations of the Research…………………………………………………... 81
5.2 Potential Future Developments………………………………………………. 81

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APPENDIX 1: ERROR MATRIX TABLES……………………………………….. .84
APPENDIX 2: VECTOR EDITING TOOLBAR .NET CODE……………………..101
REFERENCES..………………….…………………………………...……………... .106

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LIST OF FIGURES

Figure 1 – Aerial photo of Ogden study area.………..………………………………… 18
Figure 2 - Overview of study areas in relationship to the state of Utah……………….. 18
Figure 3 – Aerial photo of Salt Lake City study area…………..………………..…….. 19
Figure 4 - Ogden DOQQ study area…………………………………………………… 20
Figure 5 - Salt Lake City MDOQ study are……………………………………….…… 21
Figure 6 – Main workflow processes…………………………………………………... 23
Figure 7 – Ogden dendrogram of ISODATA clustering 10 classes..………………….. 29
Figure 8 – Ogden dendrogram of ISODATA clustering 25 classes…………………… 30
Figure 9 – Ogden dendrogram of ISODATA clustering 100 classes………………….. 31
Figure 10 – Distances between classes from Salt Lake City dendrogram…………..…. 32
Figure 11 – Training sample distribution for the Ogden image………………………...34
Figure 12 – Training sample distribution for Salt Lake City image………………….... 34
Figure 13 – Unstretched images compared to contrast stretched images……………… 36
Figure 14 – Post processing ArcGIS Model…………………………………………… 41
Figure 15 – Polygon raster to vector, smoothing, and smooth simplify………………...42
Figure 16 – Classification using visual interpretation of the Ogden image……………. 49
Figure 17 – Classification using visual interpretation of the Salt Lake City image…….49
Figure 18 – Ogden image ISODATA classifications………………………..………… 51
Figure 19 – Salt Lake City image ISODATA classifications………………………….. 53
Figure 20 – Minimum distance and support vector machine classification of the Salt
Lake City image……………………….…………………………………... 57
Figure 21 – Minimum distance classification of the Ogden image with high pass filter 58
Figure 22 – Minimum distance classification of the Ogden image with low pass filter. 59
Figure 23 – ISODATA 10 spectral classes Halounova image…………………………. 61
Figure 24 – SCRM object-based segmentation images………………………………... 64
Figure 25 – Ogden object-based classification image and post processing system
vectors……………………………………………………………………... 68
Figure 26 – Salt Lake City object-based classification image and post processing
system vectors………………………………………………………….… 69
Figure 27 – Ogden pixel based classification and post processing system vectors……. 69

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LIST OF TABLES

Table 1 – First level classification Ogden land use/land cover classes………………… 25
Table 2 – First level classification Salt Lake City land use/land cover classes………… 27
Table 3 – ISODATA overall accuracy results for Ogden and Salt Lake City study areas
………………………………………………………………………………………….. 50
Table 4 – Training sample statistics from original Ogden image…………….………… 55
Table 5 – Training sample statistics from original Salt Lake image…………………….56
Table 6 – Ogden image overall accuracy and level 1 completion time………….………72
Table 7 – Salt Lake City image overall accuracy and level 1 completion time…….…... 74
Table 8 – User’s accuracies for individual land use/land cover types Ogden study area. 76
Table 9 – User’s accuracies for individual land use/land cover types Salt Lake City study
area…………………………………………………………………………... 77
Table 10 – Overall accuracy ranges for classification groups………………………….. 78

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ACKNOWLEDGMENTS

I would like to thank Dr. Ming-Chih Hung for chairing my thesis committee and for
all the support, encouragement and guidance he has given me along the way. I would
also like to thank Dr. Yi-Hwa Wu and Dr. Patricia Drews for serving on my thesis
committee and for their contributions in developing this thesis. Last but certainly not
least I would like to thank my husband Barry for encouraging me through many long
nights and weekends while I completed this work. Without your support and love I
would never have been able to finish this thesis.

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CHAPTER 1: INTRODUCTION

Aerial imagery from the 1930s to the present is a primary data source used to study
many natural processes and land use patterns (Carmel and Kadmon 1998, Kadmon and
Harari-Kremer 1999). Early aerial imagery from the 1930s to early 1990s is
predominantly black and white (panchromatic) film photography meaning there is only
one band of data. This type of imagery contains limited spectral information unlike
today’s satellite digital sensors, which offer more spectral information even in the
panchromatic band.
The Aerial Photography Field Office (APFO) is a division of the Farm Service
Agency (FSA), of the United States Department of Agriculture (USDA). The APFO,
located in Salt Lake City, Utah, has one of the nation’s largest collections of historical
aerial imagery dating back to the 1950s. Film from the 1930s through 1940s was sent to
the national archives. APFO has over 50,000 rolls of film of which over 60% is black
and white (Mathews 2005). This historical aerial imagery is a valuable, largely untapped
resource. The film format of the imagery makes it unavailable to GIS and imagery
analysis programs unless it is scanned and processed to digital format. There is
widespread interest from the public and other government agencies in making this
imagery available and usable in digital format.
Recently, more historical imagery from the 1950s to 1990s is being scanned to digital
format for use in change detection projects for the Farm Service Agency (FSA).
According to Brian Vanderbilt (personal communication, 01 Sep 2009) FSA is interested
in studying agricultural loss patterns over long periods so that processes of change can be
more fully understood. One of the challenges with these types of projects is that land
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