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AROP

software
posted on 2024-02-15, 19:09 authored by Feng GaoFeng Gao

Accurate geo-referencing information is a basic requirement for combining remote satellite imagery with other geographic information. To detect changes in time-series satellite images, it is extremely important for the images to be precisely co-registered and orthorectified, so that images acquired from different sensors and dates can be compared directly.

Precise registration relates satellite images to the ground reference based on carefully selected ground control points between the image and corresponding ground objects. Co-registration matches two images based on the tie points in the images. The topographical variations of the earth’s surface and the satellite view zenith angle affect the pixel’s distance projected onto the satellite image. The distortion inherent in the image is determined by topographical elevation. The orthorectification process is used to correct the pixel displacement caused by the topographical variations at the off-nadir viewing and to make the image orthographic, with every pixel in its correct location regardless of elevation and viewing direction.

The automated registration and orthorectification package (AROP) uses precisely registered and orthorectified Landsat data (e.g., GeoCover or recently released free Landsat Level 1T data from the USGS EROS data center) as the base image to co-register, orthorectify and reproject (if needs) the warp images from other data sources, and thus make geo-referenced time-series images consistent in the geographic extent, spatial resolution, and projection. The co-registration, orthorectification and reprojection processes were integrated and thus image is only resampled once. This package has been tested on the Landsat Multi-spectral Scanner (MSS), TM, Enhanced TM Plus (ETM+) and Operational Land Imager (OLI), Terra ASTER, CBERS CCD, IRS-P6 AWiFS, and Sentinel-2 Multispectral Instrument (MSI) data.

The development of the AROP package was supported by the U.S. Geological Survey (USGS) Landsat Science Team project and the NASA EOS project. The package was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao (from September 2005 to June 2011). Further improvement and continuous maintenance are now being undertaken in the Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture (USDA) by Dr. Feng Gao.


Resources in this dataset:

Funding

USDA-ARS

History

Data contact name

Gao, Feng

Data contact email

Feng.Gao@ars.usda.gov

Publisher

United States Department of Agriculture

Intended use

co-register and orthorectify remote sensing imagery

Use limitations

The software package runs on Linux system and has been tested on the Landsat Multi-spectral Scanner (MSS), TM, Enhanced TM Plus (ETM+) and Operational Land Imager (OLI), Terra ASTER, CBERS CCD, IRS-P6 AWiFS, and Sentinel-2 Multispectral Instrument (MSI) data.

Theme

  • Not specified

ISO Topic Category

  • environment

National Agricultural Library Thesaurus terms

Landsat; time series analysis; United States Geological Survey; radiometry; information processing; Advanced Spaceborne Thermal Emission and Reflection Radiometer; moderate resolution imaging spectroradiometer; spatial data; leaf area index; reflectance; algorithms; surface area; remote sensing; computer software; models

OMB Bureau Code

  • 005:18 - Agricultural Research Service

OMB Program Code

  • 005:040 - National Research

Pending citation

  • No

Public Access Level

  • Public

Preferred dataset citation

Gao, Feng (2019). AROP. United States Department of Agriculture.