\chapter{Placing Items in a Virtual Reality Visualization}\label{ch3}

	The goal of this project is to place items, particularly
	trees, at locations within  the VFIRE artificial environment
	corresponding to their true locations in the actual environment.
	Meeting this goal requires that the actual locations of most
	of the trees be known.	In addition, trees, correctly placed,
	should have appearances similar to their appearances in the real
	environment, meaning that the heights, widths, and types of most
	of the trees must also be known.  A further goal of this project
	is to place artificial structures such as houses and buildings
	in their correct positions within the VFIRE environment.


	These goals are limited by the type and degree of detail of the
	available geographic image data.  The available data consist of
	satellite images and LANDFIRE vegetation maps.	The satellite
	imagery includes a panchromatic image at 1-meter resolution.
	It also includes red, green, blue, and near infrared images each
	at 4-meter resolution.  There are several vegetation maps
	available, some being of minimal usefulness and some being of
	significant usefulness for the purposes of this project.


	It is not assumed that the available data are necessarily
	adequate to fully meet the ultimate goals of the project,
	stated above.  The 4-meter resolution images do not appear
	to have sufficient detail to distinguish trees.  The 1-meter
	resolution image does appear to have marginally sufficient detail
	to distinguish trees.  However, groups of trees in different
	regions of the image have different characteristic appearances,
	and there exist many non-tree surface features in the image that
	can easily be mistaken for trees by a tree detection algorithm.
	For these reasons, this project uses an interactive process
	for tree detection, allowing the user to assist the computer in
	overcoming some of the limitations in the image data.


	This project detects trees using a template matching algorithm,
	which allows the user to quickly adapt this project to
	the way trees look in any given image.	This is possible
	because the algorithm is not tailored to a particular image.
	The user-defined template is tailored to the image, and the
	algorithm is tailored to the correlation pattern between the
	image and the template.  As long as the trees in an image can
	be grouped into collections of groupwise-similar appearance,
	the user can select one representative tree from each group
	to serve as the template for that group.  The system will then
	search for all items that look similar to the templates.


	Although the trees in the 1m-resolution image are generally
	identifiable as trees, the tree types are not easily identifiable
	without prior knowledge of the types of vegetation in the area.
	Therefore, vegetation maps are used to supplement the vegetation
	type information not conveyed by the image itself.  If the user
	clicks on the image, the system will display the information in
	the vegetation maps pertaining to the location where the mouse
	click occurred.  In addition, the user can choose to have the
	photographic image of the geographic area highlighted using the
	colors of the vegetation map.  This allows the user to see the
	dominant vegetation patterns along with the photo of the area
	at the same time.


	Photographic images have already been obtained for the current
	area of interest, Kyle Canyon in Southern Nevada.  However,
	it is possible that the project may be used in the future in
	an area for which photographic imagery will not be obtained.
	Given the low cost of vegetation maps, it is likely that they
	will be available even when no other data are available.	Therefore,
	the system is also able to place trees based on vegetation map
	data alone.  Tree placements, in this case, are made by placing
	trees randomly within each vegetation map cell according to the
	vegetation coverage, vegetation type, and tree height specified
	in the respective maps.  The tree placements produced in this way
	are less accurate than those produced from photographic imagery,
	but they are more accurate than completely random placements.


	This project does not currently allow for the automatic detection
	of artificial structures. The template matching algorithm used
	for detecting trees does not work well for detecting houses
	and buildings in the area of interest because their appearances
	exhibit a high degree of diversity, preventing the creation of
	widely applicable templates.  Furthermore, the number of such
	structures in the area is small enough to allow the user to
	efficiently place each one of them manually.  Therefore, the system
	allows artificial structures (or trees, if desired) to be manually
	placed anywhere in the scene.  It also allows items of any type
	to be manually deleted from the scene.



