Remote Sensing

Introduction The goal of this lab is to familiarize students with an image pre-processing technique known as as geometric correction. Students are able to exercise this skill using two different types of rectification pre-processing: image-to-map and image-to-image rectification. Methods For both sections, students use the Control Points tool under the Multispectral toolset to begin the geometric correction process. Using a Polynomial Geometric Model, Erdas users have the option to choose the number of polynomials they wish to model in their rectification transformation. The number of polynomials required for use may vary based on the extent of the distortion between the original and referenced image.  In Part One , the distortion that exists between the original image and the referenced map is minimal. Students use a 1st order polynomial equation to rectify the desired image. A 1st order polynomial equation requires a minimum of three Ground Control Points (GCPs) betw...

Introduction: For this lab, students experiment with LiDAR in remote sensing, learning to process and generate a number of surface models, terrain models, and intensity images through the use of point cloud data.  Methods: Part 1: Point Cloud Visualization in Erdas Imagine- In Part 1 of the lab, students upload the LAS dataset into ArcMap and examine its properties. Special notes are taken regarding information found in the Metadata and the Tile Index of the dataset. Since there is no particular projection for the original dataset, one will have to be prescribed. Part 2: Generating a LAS Dataset and Exploring LiDAR Point Clouds with ArcGIS- Using ArcCatalog, students generate a new LAS dataset in their Lab 5 folder. All LAS files used in Part 1 are selected for the new dataset, and statistics are calculated for input into the new LAS Dataset. The Metadata, examined in Part 1, revealed the intended projection to be used for the files collected in the dataset: NAD 1983 H...