Image Geometric Correction Methods

Introduction

Geometric correction is an important system that is normally performed on satellite images as a part of preprocessing a prior to the extraction of biophysical information. Geometric correction is utilized to remove geometric distortion of an image so that the pixels will be in a proper planimetric position. There are three types of geometric correction image-to-map, image-to-image and a hybrid approach that employees image to map and image to image. Geometric correction starts with a distorted image and a reference image. A collection of ground control points is collected onto the reference image to be interpolated onto the distorted image. Upon completion of the multipoint geometric correction the resulting image should be rectified

Goals

In this lab we were tasked to do two types of geometric correction image-to-map rectification and image-to-image rectification. We do both these steps with a reference image to create a planimetric image. The images that we had were skewed by internal error since the mapping satellite followed a NADIR path while the earth continued to move west to east creating a skewed version of the image. By using a reference image we can rectify this.

Methods

To create a rectified image ERDAS Imagine was utilized for this project. The first objective was to create a rectified image using a 1^st order polynomial equation in a map to image rectification. The distorted image is Chicago in digital raster format. The reference image is Chicago in USGS format. The distorted image and rectified image needs to be imported into ERDAS. Upon importation the multispectral raster tool needs to be selected. Control points need to be inserted into the distorted and reference image. The rectification can be a simple multipoint geometric correction this order of transformation is the first order. This means we need to enter a minimum of 3 GCPs.

                The GCPs need to be inserted into the distorted and reference image. Since we are using a first order transformation care needs to be taken to not insert more than 4 points to avoid crashing the tool. Upon entering the GCPs in both the reference and distorted image the Root Mean Square (RMS) error needs to be below 2.0 for correct placement, this sometimes require manually replacement of the GCP’s.

RMS error correcting

                The image can now be computed using the multipoint geometric correction tool. Since this image is using a 1^st order transformation we can use nearest neighbor interpolation. Once the image is interpolated we can see how the image is corrected into a geometric correct position.

geographically corrected on left, original on right

                The next step is to interpolate by image-to-image. This is done in the same way as the above image but with some differences. By using a reference image of Sierra Leone with a distorted image of the same area the image is rectified by using multispectral tool. For this image bilinear interpolation will be used, this means more GCPs are required due to the skewness of the image. After adding the 12 needed GCPs we are ready to interpolate. Upon running the bilinear interpolation we now have a correct image of Sierra Leone.

Bilinear corrected image on left with original on right

Results

The results of running geometric correction help to create spatially correct images in the form of interpolation. This data is useful to create correct images that are not distorted and can be evaluated without skewness.

Using Lidar Data

Lab 5: Using Lidar Data

Introduction

To create accurate and high resolution maps we need to use data that is not from Digital Elevation Models which are becoming obsolete. The data that we need to use to create highly accurate high resolution maps is known as LIDAR or Light Detection and Ranging. This data is very interesting since it using UV Visible and NIR wavelengths to map physical features by emitting a laser to the ground and measuring the return rate. Using this data we can begin to create high resolution maps that are useful for Agriculture, Geologic process, surveying and even mapping of the ocean floor. Our application of LIDAR was to create basic hill shaded Digital Terrain Models (DTM) and Digital Surface Models (DSM)

Goals

The main goal of this lab exercise was to gain basic knowledge of lidar data structure and processing. The tasks that we were required to do were the processing and retrieval of the various surface and terrain models and to use this data to process and create a variety of images and products from the point cloud developed by the lidar data.

Methods

To create these high resolution maps we first downloaded the raw Lidar data from the assigned lab folder provided. Using this data we created a new LAS data set named Eau_Claire_City. After adding the LAS files to the data set window we were able to calculate and project a coordinate system for the xyz axis. When this data was projected onto ArcMap we were able to see the point cloud returns from the data. Using this raw Lidar data we are able to warp the map based on elevation, contour lines, aspect and slope. We were also using the lidar dat to create interactive views by using the profile view tool in the LAS Toolbar

The power of Lidar, Returns of Phoenix park bridge 

We were then able to create a DSM and DTM by manipulating the LIDAR data. We used the Arc Tool box to create a raster from the LIDAR data by following the route:

Conversion tools to raster> to las dataset> to raster

This allowed us to create a DSM based on the criteria that we selected. For our models we used parameters of Binning, Cell Assignment set to maximum and natural neighbor as the void fill method with a cell size of 2 meters per pixel for the raster. After the DSM was completed we then created a Hillshade effect to the DSM.

DSM Raster before Hillshade

DSM after Hillshade

Next we created a DTM which would only show the terrain of the area we were studying. We followed the same route as the DSM but switched it to minimum cell assignment type. We also only looked at the ground return of the LIDAR data. This will ignore all other returns except for those marked by the ground effectively giving us a look at only the terrain model.

DTM Raster with Hillshade

We also created an Intensity image that is only in black and white. This is very helpful for identifying features where a lot of detail is needed.

Intensity Image

Results

The results involved us creating new and exciting LIDAR maps based on DSM, DTM and Intensity. All of these will allow us to use lidar data to our advantage. This will be helpful in geologic practices, land use surveying and slope effects in the Eau Claire area.