Remote Sensing 338

Geometric Correction Goals: This lab was an introduction to the pre processing method used when dealing with images that need to be geometrically corrected. This lab focused on  image to map rectification using a reference map to find points on the landscape and image to image registration which compared two images.   Methods: Part 1: Image to Map Rectification The first part focused on geometrically correcting an image in Erdas using the image to map rectification method.  To do this, I first opened both the image to be rectified and the reference image in Erdas Imagine. Using processing tools and the Create GCP tool we created ground control points (GCPs) which is part of the geometric correction process. This first section dealt with a 1st order polynomial transformation of images. For this 1st order polynomial transformation a minimum of three GCPs were needed. We added points until the model solution was current. These GCPs needed to be spread out over the images in

Goals: The goal of this lab is to work with LiDAR data and be able to process and retrieve various surface terrain models. Also to create and process intensity images from point cloud data. Working with point clouds in LAS data formats is another goal of this lab. Methods: In this lab Erdas Imagine GIS techniques and ArcMap were utilized. Part 1: Point Cloud Visualization in Erdas Imagine In the first part of this lab we loaded LAS files of Eau Claire into Erdas Imagine. This was to check for any errors in the data. The next step was to look over the metadata. We then analyzed the same data set  in ArcMap and further manipulated the data in this program. (Figure 1)  Figure 1. Eau Claire data in Erdas Imagine Part 2: Generate a LAS Dataset and Explore LiDAR Point Clouds with ArcGIS In this part of the lab we created a LAS dataset and explored the properties of this dataset. We then began applying visualization techniques to the LAS dataset and manipulat

Goals:  The purpose of this lab was to develop skills in image enhancement, mosaic, model building, image enhancement for visual interpretation and processing techniques. The lab work on altering the spatial resolution of images for analysis as well as enhancing the radiometric quality of images and linking Google Earth Pro and Erdas viewers in order to analyze and interpret features and their associations to the surrounding landscape.  Methods:  In this lab we used the inquiry box tools and raster tools. To enhance the spatial resolution of the satellite images we pan sharpened them using the resolution merge tool. The image was further enhanced by re sampling the pixels by using both the nearest neighbor method and the bi linear interpolation method. We also reduced the haze on an image by using radiometric enhancement using the haze reduction tool provided on the toolbar. Results: Part 1: Image Sub Setting This part looked at two sub setting methods. The firs