Introduction

In this project, I completed a full ortho mapping workflow in ArcGIS Pro to create accurate satellite imagery products. The goal was to correct geometric distortion in raw satellite images so they can be reliably used for mapping, analysis, and measurement.

This workflow included creating an ortho mapping workspace, performing block adjustment, adding ground control points, generating a digital elevation model (DEM), and producing an orthomosaic. The process follows a structured photogrammetry workflow that improves both visual alignment and spatial accuracy.

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Project Background

Satellite imagery is affected by terrain, sensor angle, and satellite movement. These factors cause distortion, which means features such as roads, shorelines, and buildings may not align correctly with maps.

Orthorectification corrects these errors by using terrain elevation data and image geometry adjustments. The final output is an orthomosaic that can support mapping, spatial analysis, and decision-making.

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Workflow Overview

1. Creating the Ortho Mapping Workspace

The first step was creating a new ortho mapping workspace in ArcGIS Pro. This workspace organizes all photogrammetric data such as sensor information, tie points, and ground control points.

At this stage, imagery is only organized and loaded. It has not yet been corrected or aligned. REM-617-01-TESPERO-Create Satel…

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2. Setting Coordinate Systems and DEM Inputs

The workflow required selecting the EGM96 geoid as the vertical coordinate system and specifying the DEM used during orthorectification.

The DEM is important because terrain elevation influences how imagery is projected. Correct elevation values help remove distortion caused by hills and valleys.

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3. Performing Block Adjustment

Block adjustment calculates tie points between overlapping images and aligns them relative to each other. This improves internal consistency before adding real-world reference points.

Common issues in this step include poor tie point matching or high residual errors if images or control points are not evenly distributed.

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4. Collecting Ground Control Points (GCPs)

Ground control points anchor the imagery to known ground locations and improve absolute positional accuracy.

Because the DEM did not fully define a vertical coordinate system, the vertical transformation was removed to ensure consistent elevation measurements during adjustment. REM-617-01-TESPERO-Create Satel…

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5. Generating a Digital Elevation Model (DEM)

After adjustment, a DEM was generated from stereo image overlap. The DEM represents terrain elevation and is essential for correcting geometric displacement.

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6. Generating the Orthomosaic

The orthomosaic combines corrected images into a seamless dataset. Terrain distortion and sensor geometry errors are removed so spatial measurements become accurate.

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7. Comparing Results Using the Swipe Tool

The Swipe tool was used to compare the original imagery against the orthorectified output.

The comparison showed improved alignment of roads, buildings, and shorelines after orthorectification. Features appeared more stable and consistent with the basemap.

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Challenges and Adjustments

The workflow itself ran smoothly, but some tutorial instructions were outdated compared to the ArcGIS Pro 3.6 interface. Some tools were located in different panels, so I had to adapt by exploring the ribbon tabs and updated workflows.

This reflects a practical reality in GIS work: software evolves quickly, and users must adapt rather than follow tutorials blindly.

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Key Lessons Learned

• Orthorectification improves positional accuracy by correcting terrain and sensor distortion.
• DEMs are critical because terrain elevation directly affects imagery geometry.
• Ground control points significantly improve absolute accuracy.
• Quality control and visual comparison tools help confirm successful corrections.

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Final Reflection

This project strengthened my understanding of photogrammetry workflows in ArcGIS Pro. More importantly, it showed how careful preparation, terrain data, and control points transform raw imagery into reliable spatial data.

For GIS professionals, orthorectification is not just a processing step. It is a foundation for producing imagery that can support real-world mapping, infrastructure planning, and analysis.