Lab 8: Spectral Signature Analysis

Goal:
 The main goal of this lab was for us to gain experience with measuring and interpreting spectral reflectance or signatures of different features that are found on the Earth's surface and near different surface materials that have been captured by satellite images. The outcome of this lab is for us to be able to collect multiple spectral signatures from remotely sensed images, look at the bands of each feature analyze them and graph them.To practice these skills we used Landsat ETM+ images of Wisconsin and we were to digitize and record the different spectral signatures of the various features below.

To find all of these spectral signatures, all we had to do was upload the image then go to the draw tab on the application and click on polygon. We needed to draw a polygon around any land or feature that was on that list or that one was interested in using the signature editing tool on. The signature editing tool is located under the raster tab, after collecting all of the data that was on the list this is what my graph ended up looking like when comparing all of the features that I had collected.

After all of the data was collected and then projected on the graph above, one can tell which features are absorbing the most energy and that plants generally reflect the most green light since they are absorbing the longer wavelengths. This lab was one of the more helpful labs, I will be able to use this skill in my future with remote sensing and in our final project, because this skill helps us track the function of different systems that are found in the environment.

Photogrammetry

Lab 7: Photogrammetry

Goal:

                The main goal of this lab was to help us develop our skills in performing key photogrammetric tasks on aerial photographs and satellite images. But to get more in depth on the goal we are more specifically training our overall understanding in the mathematics behind certain calculations such as photographic scales, measurement of areas and perimeters of features, and calculating the relief displacement of an image. To sum up what the overall goal that we are trying to learn are different ways to look at images on Erdas and this lab is going to help us perform Orthorectification on different satellite images as well as introduce us to stereoscopy.

Methods:

                While going through the lab, I had to use many different tools that we have never used before on Erdas Imagine before and we had to perform multiple tests on different images in order to complete different tasks in the lab. The first step of the lab had to do with scales, measurements and relief displacement. The first question was asking us to create scales for different images based off of different numbers that were given to us. We had to calculate what the scale was for an aerial photograph while showing all of our work. We had to go through this procedure a couple of different times for different images and different areas on an image. After we found out all of the measurements we had to calculate the relief displacement of a certain object on a map or in this case the smoke stack of a building. In order to find the relief displacement we need to use this equation; d = (h*r)/H, this allows us to see which objects have a higher elevation than the local datum.

                The next step started going into less measurements and scales, and more stereoscopy. This procedure involved a 3D perspective view of the City of Eau Claire. We used this data that we collected about the elevation of the city of Eau Claire to determine which features have the highest elevation and which had the lowest. In order to figure out that we need to use a new tool called the Anaglyph Generation of two different images. This would give us an image that would show the different areas in the City of Eau Claire that have a higher elevation of different areas. We needed to use Polaroid glasses to observe the image and the surrounding areas of the image this would help us describe the elevation of the different features in Eau Claire.

                The final step was Orthorectification, this part of the lab will introduce us to Erdas Imagine Lecia Photogrammetric Suite or LPS which is mainly used in digital photogrammetry for many different reasons, some might include triangulation, Orthorectification of images collected, extraction of digital surface and elevation models etc. In order to do this we needed to use the Imagine Photogrammetry tool in the toolbox, from here we created a new block file so than we can begin to set up the model. What we are working on currently is going to be a horizontal reference coordinate system instead of a vertical we will be working on the vertical reference coordinate system later in the lab. Then we had to start by adding the Spot_pan.img to the program, after that we needed to make sure all of the properties for the program were correct and set to the right setting. The next step was to start activate point measurements and collecting GCPs by comparing to images that were showing the same area. Then we would just keep adding on points with different reference points that would show the same image on both maps. We had to do this procedure nine times in a row to get all of the reference points that were needed, than we had to reset the horizontal reference source and edit an entirely different images than before while keeping the one of the original images. Then we would go on to add some more reference points to both images points eleven and twelve.

                The next step was to change it to a Vertical Reference source, while using a DEM and update all of the Z values on the selected points that we have made so far. We then changed the “Type” and “Usage” columns from “none” and “tie”, to “full” and “control”. After that we closed the point measurement tool and were returned to the LPS project manager. This is where we started adding more reference points to these two images comparing the two through the use of these points. We had to add twelve more reference points to the two images, this will lead us to starting to triangulation and ortho resampling. For the triangulation we needed to use same weighted values for all of the points. The next step was to create a Triangulation Summary Report to see if we would be able to actually triangulate the data that we are working on. Once we finished with the final steps for the triangulation we started the ortho resampling process, when starting the final process we were using a Bilinear Interpolation as the resampling method, our end goal was to try and get all of the reference points from both instances onto the same block figure. The final image will be of two images that are displaying the same image but at different locations, this meaning once you put the two maps over one another it will have a special overlap at the boundaries of the two orthorectified images.