Lab 8: Spectral Signature Analysis

Background and Goals

The goal of this lab is to introduce students to the process of analyzing and collected various spectral signatures from satellite images. This includes digitizing areas of different materials in order to collect their unique spectral signature and graphing and analyzing the results. Along with creating NDVI images and analyzing ferrous mineral distribution.

Methods

To begin the lab, I brought in an image Western Wisconsin. Once the image was into the viewer I digitized AOI’s (Areas of Interest) across many different land surface features. These features included agricultural fields both planted and un-planted, different types of forest, rocks, and urban development. Once the AOI’s were created I brought them into the Signature Editor (Figure 1)where further analysis could be done. From the Signature Editor I then proceeded to graph the different materials and analysis their signatures across different bands. I then compared the different materials spectral signatures against each other both amongst similar features and amongst features that are different (Figure 2).

Figure 1. Signature Analysis Table

Figure 2. Graph showing the different spectral signatures

After analyzing the different spectral signatures that different features reflect, the next task that I was given was to perform resource monitoring in the form of vegetation health monitoring by creating a NDVI (Normalized Difference Vegetation Index) image and Ferrous Mineral content image of Eau Claire-Chippewa Falls area.  To this I brought in an image of the area and used the NDVI tool to create the new NDVI Image and the Indices Tool to create an image that showed the abundance of Ferrous Minerals. The new images where in black and white so I imported the images into ArcMap and classified the data and created the maps below.

Figure 3. Map displaying the NDVI for the Eau Claire-Chippewa Falls Area

Figure 4. Map displaying the distribution of Ferrous Minerals in the Eau Claire-Chippewa Falls Area

Sources

Earth Resources Observation and Science Center, United States Geological Survey

Lab 7

Goals and Background

The goal of this lab was to demonstrate the ability to perform a variety of photogrammetric tasks on satellite and other aerial images. This includes calculating the mathematics behind calculating scales, area of objects, and relief displacement. The lab also includes stereoscopy, creation of anaglyph images, and orthorectification of aerial and satellite images.

Methods

The first section of the lab involved calculating scales of images, measurements of objects, and relief displacement for distorted objects in images. To calculate scales for aerial images I used the height at which the image and the length of the focal lens of the camera to compute the scale of the images. To calculate the area of objects I used the Measure Perimeters and Areas tools to digitize polygons around features and record their measurements. To measure the relief displacement of a smoke stack located on campus, I measured the height of the tower, the height of the camera, and the radial distance from the top of the tower to the principal point which I measured to be .38 inches.

The second section of the lab involved reading stereoscopic images and creating anaglyph images. To begin this section I was given two images, one had relief displacement and the other had already corrected. The objective of this section was to point out the distortions and come those distortions to the corrected image. Bringing in the two images into two separate viewers and zooming into the same objects in both images. When looking at the two images side by side it was obvious the image on the left clearly had relief displacement errors. The next section of the lab involved created two anaglyph images. The first anaglyph image was created using images that had relief displacement and the second was an image was created using a Digital Surface Model (DSM) created from a LiDar point cloud. (Figure 1) After creating the two anaglyph images and looking at them with 3D glasses, it was obvious the effects that relief displacement can cause on the accuracy of stereoscopic image. The first image was severely distorted and elevation changes were very exaggerated and unrealistic compared to the second anaglyph derived from Lidar data.

The next section of the lab involved Orthorectifying images. This involved creating a new block file in Erdas Imagine and setting accurate parameters for that block file so that the orthorectification process could be done correctly. I choose the correct Horizontal Reference Coordinate System, Spheroid, Datum and Projection Type. Once the parameters were correctly inputted into the model I then began to bring the images and collect Ground Control Points (GCP’s) (Figure 2). After collecting GCP’s from multiple images and storing the GCP’s in the block file and verifying the accuracy of the points, I used the Automated Tie Point generator to create tie points based off of the GCP’s that I collected earlier. After running the tool and verifying that the tie points were in the correct position, I used the Ortho Resampling Process tool to complete the Orthorectification process. Once was tool was completed, I brought the new images into a new viewer (Figure 3).

Results

The images below show the results from the lab above.

Figure 1. Anaglyph Created using a Digital Surface Model

Figure 2. Collection of GCP's during the Orthorectification Process

Final 3. Final Orthorectified Image

Sources

National Agriculture Imagery Program (NAIP) images are from United States Department of Agriculture, 2005.

Digital Elevation Model (DEM) for Eau Claire, WI is from United States Department of Agriculture Natural Resources Conservation Service, 2010.

Lidar-derived surface model (DSM) for sections of Eau Claire and Chippewa are from Eau Claire County and Chippewa County governments respectively.

Spot satellite images are from Erdas Imagine, 2009.

Digital elevation model (DEM) for Palm Spring, CA is from Erdas Imagine, 2009

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National Aerial Photography Program (NAPP) 2 meter images are from Erdas Imagine, 2009.