Remote Sensing Lab 6

Goals

This lab is designed to introduce us to a very important image preprocessing exercise known as geometric correction. The lab is structured to develop our skills on the two major types of geometric correction that are normally performed on satellite images as part of the preprocessing activities prior to the extraction of biophysical and sociocultural information from satellite images. 

Methods

The first part of this lab was dealing with image-to-map rectification. We started off in Erdas Imagine and were looking at the Chicago_drg.img image. We compared it to the Chicago_2000.img. We needed to use the first order polynomial equation to geometrically correct the Chicago_2000.img. We used the Multipoint Geometric Correction window to place GCPs on both maps. Because this was a first order, we only needed to use four GCPs. Once all the points were placed, we needed to make slight adjustments to their placements so that we could lower our Root Mean Square error. For this part, we were just supposed to reduce it below 2.0. The image below is the comparison between the original image and the corrected image. 

In par two of the lab, we did a similar exercise. We were using image to image registration. We were working with two different images of Sierra Leone. One of the images had some pretty serious distortion that needed to be fixed. We did the same process where we opened the Multipoint Geometric Correction window. The difference here was that we changed it to a 3rd order polynomial. This meant that in order for it to work, we needed to have at least ten GCPs on each map. Because of this, when we finished plotting points, our RMS error was very high. We had to work with all the points and adjust them ever so slightly  in order to achieve an RMS error below 1.0. I was actually able to get my RMS error to .0125 which I felt pretty good about. The image below is the screen shot of both images with their GCPs in place. 

Results

From this lab, we learned how to deal with some of the distortion that we might find in images that we are working with. Although this was a shorter lab compared to our other ones, geometric correction is a very important tool when working with remote sensing images. This lab helped us work through and develop more accurate images. 

Lab 5

Goal

The goal of this lab is to gain basic knowledge on Lidar data structure and processing. Some of the objectives are processing and retrieval of various surface and terrain models, and processing and creation of intensity image and other derivative products from point cloud. 

Methods

Part one of lab 5 was "Point cloud visualizatoin in Erdas Imagine". In this section we used Erdas to view a lidar point cloud file. We looked at things like the metadata and tile index. We then opened the same image in ArcMap because it gives a better interface to work with the image. 

The real fun started in Part two of the lab. The title was, "Generate a LAS dataset and explore lidar point clouds with ArcGIS. We were supposed to pretend that we were a GIS manager working on a project for the City of Eau Claire. We had acquired lidar point cloud in LAS format for a portion of the City. We first wanted to initiate an initial quality check on the data by looking at its area and coverage, and also verify the current classification of the lidar. We were given the tasks of creating a LAS dataset, explore the properties of the dataset, and visualize the LAS dataset as point cloud in 2D and 3D. 

We started off by creating a LAS folder and a dataset in the folder. We added in data that was given to us. From there, you could look at all the data and observe all of the values that came with it. We added a coordinate system to the dataset for both the XY coordinate system and the Z system. Once the dataset was finished, we brought it into ArcMap and explored the data. Looking at the elevation and point density on the map. 

In Part 3 of the lab, we worked on the "generation of lidar derivative products". This meant that we were deriving DSM and DTM products from our point cloud data. We used tools in ArcMap to create our DSM, DTM, and Hillshade images. The image below, is one of the Hillshade images

 In the last section of our lab, we derived a lidar intensity image from point cloud data. The image below is a screenshot of my lidar intensity image taken from Erdas Imagine.






















Sources

All data used was given to us and explored in ArcMap and Erdas Imagine.