Ortho Image Processing
Image geo-referencing, mosaicking, and ortho correction are important processing steps preceding the analysis of the
collected data. Ortho Imagery, as opposed to raw imagery, allows for the simple determination of geographic location,
area, distance, and rates of change with repeat coverage. It further allows the image information to be fused with
satellite, elevation, and map data to analyze relational patterns and trends of features of interest.
NEOS uses WAAS enabled flight GPS/IMU navigation data in defining ground control points on the fly for automated
ortho image processing. The method is fast, repeatable, and adaptable to different terrain for "Intelligence at a
Glance". Image GPS ID tags are immediately viewed in Google Earth to identify stereo images for 3D analysis.
collected data. Ortho Imagery, as opposed to raw imagery, allows for the simple determination of geographic location,
area, distance, and rates of change with repeat coverage. It further allows the image information to be fused with
satellite, elevation, and map data to analyze relational patterns and trends of features of interest.
NEOS uses WAAS enabled flight GPS/IMU navigation data in defining ground control points on the fly for automated
ortho image processing. The method is fast, repeatable, and adaptable to different terrain for "Intelligence at a
Glance". Image GPS ID tags are immediately viewed in Google Earth to identify stereo images for 3D analysis.
Process Overview
There are five main steps in the process of
creating precision ortho image products:
(1) interpolation of navigation data and
individual frames of imagery;
(2) finding the interior orientation parameters
for the camera system;
(3) finding the relative orientation parameters
of the image frames to each other;
(4) finding the absolute orientation of the
image to a ground coordinate system; and
(5) re-sampling the image to a ground
coordinate system.
There are five main steps in the process of
creating precision ortho image products:
(1) interpolation of navigation data and
individual frames of imagery;
(2) finding the interior orientation parameters
for the camera system;
(3) finding the relative orientation parameters
of the image frames to each other;
(4) finding the absolute orientation of the
image to a ground coordinate system; and
(5) re-sampling the image to a ground
coordinate system.
Ortho Imagery on the Fly Seamless Ortho Mosaics
Data Fusion: Relational Risk Analysis Change Detection
Image Registration Errors
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