Identification of Tree Locations in Geographic Images A thesis submitted in partial fulfillment of...

Identification of Tree Locations in Geographic Images

A thesis submitted in partial fulfillment of therequirements for the degree of Master of Science

in Computer Engineering

By

David Brown

Dr. Frederick C. Harris, Jr., Thesis Advisor

December, 2008

Committee

Dr. Frederick C. Harris, Jr.

Dr. Sergiu M. Dascalu

Dr. Timothy J. Brown

Overview

Purpose Background Methods Software Specification and Design Implementation and Results Conclusion and Future Work

Purpose

Purpose

To create an item placement utility for VFIRE (Virtual

Fire In Realistic Environments)

VFIRE is a virtual reality application for visualizing

wildfire simulations.

Current area of interest is Kyle Canyon in Southern

Nevada.

Purpose

Main uses of VFIRE:

Fire Training

Fire Planning

Fire Model Verification

Wildfire Visualization [25]

Purpose

The placement of items in the visualization should

correspond to their locations in the real environment.

This utility is intended to place large numbers of trees

with reasonable speed and accuracy.

It can also be used to place a small number of houses with

reasonable speed and accuracy.

Background

Background – Geographic Images

Photography

Standard Color

Panchromatic

Multispectral

Hyperspectral

Color Infrared (CIR)

Background – Photography

CIR images look different from most other types

True Color Image [5, page 45] False Color Image [5, page 45]

Background – Vegetation Maps

Vegetation data can be displayed as a map.

Vegetation maps have been created by LANDFIRE to

show various attributes.

Map of Vegetation TypeMap of Vegetation Cover

Background – Point Operations

Each output pixel is based on a single input pixel.

Changing the brightness of an image is a point operation.

Image After Increasing BrightnessOriginal Image

Background – Neighborhood Operations

Blurring an image is a neighborhood operation.

The blur filter is applied to each input neighborhood.

Original Image Blurred Image

Background – Edge Detection

The LoG (Laplacian of Gaussian) Filter is an edge

detection filter in which the level of detail can be

controlled.

Laplacian of Gaussian (LoG) filter [36]

Background – Template Matching

Can be used as the first step in image analysis

Used when finding the location of known item

Neighborhood operation where the filter mask is a

template of the desired item

Filtering produces a correlation image that can be scanned

for bright spots.

Background – Virtual Reality [2]

Requirements:

Virtual World

Immersion

Sensory Feedback

Interactivity

Background – HMDs [2]

Head Mounted Displays

100% Field of Regard

May cause dizziness

Only one person can view

at one time

A Head Mounted Display [2, page 14]

Background – Multi-Sided Projection Displays [2]

Field of regard depends on the

number of sides.

Wider field of view than HMDs

No dizziness

Many people can view at once

More bulky and expensive than

HMDsThree-Sided Projection Display [25]

Background – Head Tracking

• View must be adjusted for

location and orientation of

head.

• Stereoscopic display can be

used to create depth

perception. Head-Tracking Active Stereo Goggles [17]

Background – Input Devices

• A wand is a commonly used

input device for virtual

reality systems.

Virtual Reality Wand [17]

Background – Related Work

Applications

Plantation Management

Assessing Forest Health

Harvestable Lumber Estimation

Fuel Load Estimation


Culvenor [6]

Even-Aged Mountain Ash

(Eucalyptus)

NIR Selected from CIR

Identify Local Maxima

Identify Local Minima

Cluster Intermediate Pixels


Pouliot et al. [33]

Uniformly Spaced Spruce

Trees in an Arboretum

Absolute Difference of NIR

and Red Bands

Moving Window, Local

Maximum Filtering


Brandtberg and Walter [11]

80-Year-Old Stands of Scot pine,

Norway spruce, birch, and aspen

Perform Scale-Space Edge

Detection to Extract Tree Crown

Perimeters

Analyze Perimeter Curvatures to

Estimate Centroids

10-cm, CIR Brightness Scale Space Image

Estimating CentersAfter Edge Detection


Larsen [23]

Image of Norway spruce

Template Created from Ray-Traced

3D Tree Model

Model Incorporates Aircraft Position,

sun position, and Spiecies-Specific

Light-Scatering Parameters

Norway Spruce

3D Template Model


Image Analysis Software [4]

Will perform template matching


Image Analysis Software

Not likely to output locations in geospatial coordinates

Not likely to provide geospatially aligned overlays of

vegetation maps

Not likely to display vegetation map data for selected

locations

Not likely to make placements based on vegetation maps

Methods

Methods

Goals:

Achieve adequate tree-placement accuracy using whatever

images (if any) are available.

Enhance Accuracy using vegetation maps.

Make tree placements using vegetation maps alone if no

photographic image is available.

Methods

System:

Interactive (not fully automatic)

Template Matching (no image constraints)

Templates Created at Runtime (quickly create multiple

templates)

Vegetation maps provide information about terrain.

Placements can be made based on vegetation maps alone.

Methods

System:

The algorithm is not tailored to any particular image.

The user-defined templates are tailored to the image.

The algorithm is tailored to the correlation image

produced using the templates.

Methods

Data for Kyle Canyon

4-Meter Photographic GeoTIFFs Red, Green, Blue, NIR

1-Meter Photographic GeoTIFF Panchromatic

5-Meter Vegetation Maps Sampled from 30-Meters Vegetation cover, vegetation type, vegetation Height

Methods

1-Meter Panchromatic Image

Trees look like blobs.

Species, size, shadow, and density are different in

different parts of the image.

Software Specification and Design


Use Cases


System consists of five

groups of global functions

using two existing

libraries.

Implementation and Results


Detection Process

• The user selects a tree to

use as a template.

• The tree is the gray blob.

• The shadow is the dark,

elongated region. Cite of First Template (Zoomed In)


Detection Process

• The user draws a

highlighting mark over the

tree and shadow.

Template Defined by User


Detection Process

• Area Near Template,

Zoomed Out

Area Near Template (Zoomed Out)


Detection Process

• Correlation Image Stored

in Red Buffer of

Workspace Image

• Other buffers are used for

intermediate processing. Correlation Image


Detection Results

• Detection Results Using a

Single Template

Result Using One Template


Detection Process

• User controls tuning

parameters for tree

detection .

Tuning Parameter Window


• Detection Process

• User specifies data for trees

associated with each

template.

• Locations, types, etc are

then written to file.

Preparation to Create Output File


Detection Process

• Entire Image of Kyle

Canyon (8km × 6km)

Entire Kyle Canyon Image


Detection Process

• Vegetation Map of Same

Area (8km × 6km)

Entire Vegetation Map


Detection Process

• Vegetation Map As

Overlay onto Image

(8km × 6km)

Overlay of Vegetation Map onto Photographic Image


Detection Process

• Text Output Resulting

When User Clicks on

Image

Text Output from Clicking on Image


Partially Random Placement

• Tree Placements Made

According to Map of

Vegetation Coverage,

Without Using Image

Partially Random Placements


Partially Random Placement

• User can control how

placements are made when

no image is available

Options for Partially Random Placements


• Detection Accuracy

• Approach is to balance false positive and false negative

errors.

• This can be done by using multiple templates.

• Trees used as templates may differ only in the shadows

they cast.


Detection Process

• The user selects a second

tree to use as a template.

• It is similar to the tree in

the first template, but the

shadow is different. Addition of Second Template


Detection Process

• Adding a second template

reduces the number of

false negative errors and

slightly increases the

number of false positive

errors.Addition of Second Template


Detection Results

• Approximately 15% Poor

• Approximately 70%

Adequate

• Approximately 15% Good

Addition of Second Template


Detection Results

• Area near templates is an

example of “adequate”

detection accuracy.

Addition of Second Template


Detection Results

• Burnt trees may be

mistaken for living trees.

• Small Number of False

Positive Errors

Burnt Trees Incorrectly Marked


Detection Results

• Small trees or shrubs

mistaken for the medium-

sized trees being sought

• About ten percent of the

image has similar terrain. Apparent Shrubs Incorrectly Marked


Detection Results

• Trees incorrectly placed on

top of houses

• These false positive errors

are very noticeable.

Rooftops Incorrectly Marked


Detection Results

• Trees incorrectly placed on

roads

• These false positive errors

are very noticeable.

Roads Incorrectly Marked


Detection Results

• Area with few errors

• Good separation between

trees makes them easier to

detect.

Good Result


Detection Results

• Areas hidden by shadow

• Accuracy is unknown

Unknown Accuracy in Hidden Areas

Conclusions and Future Work

Conclusions

• The purpose of this utility is to achieve adequate tree

detection results using any available image.

• This goal cannot always be accomplished.

• Using the 1-meter panchromatic image of Kyle Canyon

produced at least “adequate” results in approximately

85% of the area of interest.


Conclusions

• The process took a few hours, including the time required

to manually delete false positive errors located in

conspicuous areas.


Future Work

• Ability to delete large numbers of trees at once

• Ability to change tuning parameters for the same

template in different regions

• Minimum Item Radius tuning parameter appears to have

little effect and may not be necessary.


Future Work

• Ability to transfer templates from one group to another

• Ability to change the highlighting colors

• Ability to scroll image without momentary pauses and

without greatly increasing memory consumption

• Ability to detect artificial structures

Questions?

Identification of Tree Locations in Geographic Images A thesis submitted in partial fulfillment of...

Documents

Transcript of Identification of Tree Locations in Geographic Images A thesis submitted in partial fulfillment of...