2018 Maine NAIP Digital Ortho Photo Imagery

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DOQQ Production Process Description; USDA FSA APFO NAIP Program 2018; State: Maine. The imagery was collected using the following digital sensors: Leica ADS-100 (Serial

Number 10541), Leica ADS-100 (Serial Number 10542), Leica ADS-100 (Serial Number 10548),

with Flight and Sensor Control Management System (FCMS) firmware: v4.57, Cameras are

calibrated radiometrically and geometrically by the manufacturer and are all certified by the

USGS. Collection was performed using a combination of the following twin-engine aircraft with

turbines flying at 16,457 ft above mean terrain. Plane types: AC90, C441, Tail numbers: N167R,

N441CJ, N441MD, With these flying heights, there is a 30% sidelap, giving the collected data

nominal ground sampling distance of 0.40 meters at 16,457 feet. Based-upon the CCD Array

configuration present in the ADS digital sensor, imagery for each flight line is 20,000-pixels in

width. Red, Green, Blue, Near-Infrared and Panchromatic image bands were collected. The

ADS 100 has the following band specifications: Red 619-651, Green 525-585, Blue 435-495,

Near Infrared 808-882, all values are in nanometers. Collected data was downloaded to

portable hard drives and shipped to the processing facility daily. Raw flight data was extracted

from external data drives using XPro software. Airborne GPS / IMU data was post-processed

using Inertial Explorer software and reviewed to ensure sufficient accuracy for project

requirements. Using Xpro software, planar rectified images were generated from the collected

data for use in image quality review. The planar rectified images were generated at native

resolution using a two standard deviation histogram stretch. Factors considered during this

review included but were not limited to the presence of smoke and/or cloud cover, contrails,

light conditions, sun glint and any sensor or hardware-related issues that potentially could

result in faulty data. When necessary, image strips identified as not meeting image quality

specifications were re-flown to obtain suitable imagery. Aero triangulation blocks were

defined primarily by order of acquisition and consisted of four to seventeen strips.

Image tie

points providing the observations for the least squares bundle adjustment were selected from

the images using an auto correlation algorithm. Photogrammetric control points consisted of

photo identifiable control points, collected using GPS field survey techniques. The control

points were loaded into a softcopy workstation and measured in the acquired image strips. A

least squares bundle adjustment of image pass points, control points and the ABGPS was

performed to develop an aero triangulation solution for each block using XPro software. Upon

final bundle adjustment, the triangulated strips were ortho-rectified to the digital elevation

model (DEM). The most recent USGS 10-meter DEMs were used in the rectification process.

Positional accuracy was reviewed in the rectified imagery by visually verifying the horizontal

positioning of the known photo-identifiable survey locations using ArcGIS software. The red,

green, blue, and infrared bands were combined to generate a final ortho-rectified image strip.

The ADS sensor collects fourteen bit image data which requires radiometric adjustment for

output in standard eight bit image channels. The ortho-rectified image strips were produced

with the full 16 bit data range, allowing radiometric adjustment to the 8 bit range to be

performed on a strip by strip basis during the final mosaicking steps. The 16 bit data range was

adjusted for display in standard eight bit image channels by defining a piecewise histogram

stretch using OrthoVista Radiometrix software. A constant stretch was defined for each image

collection period, and then strip by strip adjustments were made as needed to account for

changes in sun angle and azimuth during the collection period. Strip adjustments were also

made to match the strips histograms as closely as possible to APFO specified histogram metrics

and color balance requirements. Automated balancing algorithms were applied to account for

bi-directional reflectance as a final step before the conversion to 8 bit data range. The imagery

was mosaicked using manual seam line generation in OrthoVista.