2017 Pennsylvania NAIP Digital Ortho Photo Imagery

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Digital Georectified Image. | Source Geospatial Form: remote-sensing image | Type of Source Media: UnKnown

DOQQ Production Process Description

USDA FSA APFO NAIP Program 2017

State: Delaware

The imagery was collected using the following digital sensors:

Leica ADS-100 (Serial Number 10547)

Leica ADS-100 (Serial Number 10564)

with Flight and Sensor Control Management

System (FCMS) firmware 4.54.

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:

Turbines flying at 16,410 ft above mean terrain

(tail number: AC90 N167R)

(tail number: C441 N87WS)

With these flying heights, there is a 27% sidelap,

giving the collected data nominal ground sampling distance

of 0.40 meters at 16,410.

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 INYS,

PosPac and/or TerraPos software and reviewed to ensure

sufficient accuracy for project requirements.

Using Inpho software, planar rectified images were

generated from the collected data for use in image quality

review. The planar rectified images were generated at five

meter 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 in to 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 Pictovera 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 twelve 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 12 bit data range, allowing radiometric adjustment

to 8 bit range to be performed on a strip by strip basis

during the final mosaicking steps.

The 12 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.