Spectral Signature Analysis

Goal and Background:

The goal of this lab was to show my ability to measure and interpret spectral reflectance of various Earth surface and near surface materials captured by satellite images. I collected spectral signatures from remotely sensed images, graphed them, and performed analysis on them to verify whether they pass the spectral separability test. 

Methods:
I opened a Landsat ETM+ image from 2000 of the Eau Claire area in Edras Imagine. I utilized the polygon tool under the Drawing tab to digitize an area in Lake Wissota. I then used the Signature Editor tool under Raster-Supervised.

In the Signature Editor I clicked Create New Signature from AOI. I changed its name and color and then displayed the mean plot window. I repeated these steps until I had spectral signatures for the following features:

• Standing Water
• Moving Water
• Vegetation
• Riparian Vegetation
• Crops
• Urban Grass
• Dry Soil (uncultivated)
• Moist Soil (uncultivated)
• Rock
• Asphalt highway
• Airport runway
• Concrete surface (Parking Lot)    

 
Results:

 Figure 1. This image demonstrates all of the spectral signatures collected together in the Signature Editor.

Figure 2. This image demonstrates the mean spectral signatures of the dry and moist soils. They follow each other almost parallel, but the difference between the bands is greatest around Band 5: 1.55-1.75 micrometers. This is likely due to the water in the moist soil, as water absorbs higher amounts of NIR, MIR, and FIR wavelengths.  

Figure 3. This image demonstrates all of the mean spectral signatures that I collected. Most of the vegetation is similar across the spectral channels, which makes sense because most plants absorb and reflect the same wavelengths of light. The asphalt, rock, soils, and runway are all fairly similar and reflect close to the same bands. This is most likely because they are made of similar materials and minerals. The water is the most different in its spectral signature because it reflects the least amount of infrared light. 

Sources:  

United States Geological Survey. (2000). Earth Resources Observation Science Center.

Photogrammetry

Goal and Background:

The goal of this lab was to show my ability to perform different photogrammertric tasks on aerial photographs and satellite images. These tasks were accomplished through the understanding of the calculation of photographic scales, the measurement of areas and perimeters of features, and the calculating of relief displacement, as well as using stereoscopy and performing orthorectification. 

Methods:
Part 1: Scales, measurements, and relief displacement
Section 1: Calculating scale of nearly vertical aerial photographs
I was given the ground distance between point A and B to be 8822.47 ft. I measured the distance between those two points on an aerial image of the same area on my screen and determined the scale mathematically as follows:

 2 inches on the photo, 8822.47 feet in real world 
2in/(8822.47ft*12)=1in/X 
2X=105869.64in 
X=105869.64in/2 
X=52934.82in  
Scale= 1: 52934.82  

I was then given an image taken by a high altitude reconnaissance aircraft of Eau Claire County. The aircraft took the image at 20,000 ft about sea level with a focal length lens of 152 mm. The elevation of Eau Claire County is 796 ft. With this information I determined the photograph's scale mathematically as follows: 

f= 152 mm

H=20,000 feet asl

H= 796 feet

S= f/ (H-h)

S=152mm/(20,000ft-796ft)

S= 5.98 in/(240000in-9552in)

S=0.0000259in

25949/1000000000 =1/X

25949X=1000000000

X=1000000000/25949

X= 38537.13

Scale= 1:38537.13 

Section 2: Measurement of areas of feature on aerial photographs
I utilized the 'Measure' tool to draw a polygon around a feature in the provided image. After digitizing, I was able to determine the feature's perimeter and area in different units.

Section 3: Calculating relief displacement from object height
I was tasked with determining the relief displacement of a smoke stack in a aerial image. The height of the aerial camera above datum was 3980 ft and the scale of the aerial photograph is 1:3209. I determined the relief displacement mathematically as follows:

(h_r*r)/ h 
(1604.5in*10.5in)/ (3980ft*12in) 
=0.352 in

Part 2: Stereoscopy
I used the tool Terrain-Anaglyph in Erdas Imagine and input an image with a 1 meter spatial resolution and another image that was the digital elevation model (DEM) of the same area with a 10 meter spatail resolution. I increased the vetical exaggeration to 2 and ran the model. Using polaroid glasses I was able to observe differences in the elevation characteristics of my anaglyph image product.  

Part 3: Orthorectification
Section 1: Create a new project
I opened LPS Project Manager through Toolbox-IMAGINE Photogrammetry. I created a new block file and chose Polynomial-based Pushbroom and SPOT Pushbroom as my Geometric Model Category. I set my projection type as UTM, a spheriod name of Clarke 1866, and a UTM Zone field of 11.

Section 2: Add imagery to the block and define sensor model
I added a frame to images folder and added my first image. I reviewed the sensor information of my image.

Section 3: Activate point measurement tool and collect GCPs
I used the Start point measurement tool and selected Classic Point Measurement Tool. I then input a reference image and used the viewer as a reference. I then collect 2 GCPs. After the first 2 GCPs I selected the Automatic (x,y) Drive icon. Then I collect more GCPs until I had 9 in total.

After my ninth GCP, I reset my horizontal reference sources to a different image and collected 2 more GCPs. I then chose the Reset Vertical Reference Source icon and chose DEM and set the DEM to my reference image file. I right-clicked the Point # column on the reference cell array and selected all. I then clicked on the update Z valueson selected points icon and updated them to match my reference image.
 
Section 4: Set type and usage, add a 2nd image to the block and collect GCPs
I left clicked the column labeled Type to highlight it and then right clicked it to access the column options and selected Formula. I then typed Full and applied the change. I repeated these steps for the Usage column, typing Control instead of Full. 

I then loaded my second image into LPS and checked its frame properties. I selected the Classic Point Measurement Tool and collected GCPs from my second image using my first image as the reference for the GCPs. I collected all the points the two images had in common.

Section 5: Automatic tie point collection, triangulation, and ortho resample
I used the Automatic Tie Point Generation Properties icon from the Point Measurement tool palatte and set the image used to all available and the initial type to Exterior/Header/GCP. I set the Intended Number of Points/Image to 40. After running I checked a number of points for accuracy.

In the Imagine Photogrammetry Project Manager I used Edit-Triangulation Properties and changed the Iterations with Relaxation value to 3. Under Ground Point Type and Standard Deviations I selected Same Weighted Values under the Type field. I then changed the X,Y, and Z fields to 15 and then ran the triangulation. I saved the report for future use.

I used the Start Ortho Resampling Process icon in the IMAGINE Photogrammetry Project Manager. I set the DTM Source to DEM and input my DEM image file. In the Output Cell Sizes field for X and Y I put 10. Under the Advanced Tab I verified that the resampling method was Bilinear Interpolation and then clicked the Add button. I input my second image and checked Use Current Cell Sizes. I then ran the Ortho Resampling.

Section 6: Viewing the orthorectified images
I brought both my images into Edras Imagine to view. 
 
Results:

 Figure 1. This image demonstrates the results from part 2. This image appears to be a fairly good representation of the elevation features of Eau Claire County. In some instances the trees and buildings appear to be much higher than they are in reality and this could be do to stereoscopic parallax that arose from using a DEM with a 10 meter spatial resolution and a base image with a 1 meter spatial resolution. 

Figure 2. This image demonstrates the results from part 3. There is a slight stair-step effect between the two images when the viewer is zoomed in close. The features of the images align nearly perfectly and there is only a thin black line between the two images when zoomed out further. 

Sources:  

Edras Imagine. (2009). Digital Elevation model (DEM) Palm Spings, CA.

Edras Imagine. (2009). Spot Satellite Images.

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

United States Department of Agriculture . (2005). National Agriculture Imagery Program (NAIP).

United States Department of Agricultutre Natural Resources Conservation Services. (2010). Digital Elevation Model (DEM) for Eau Claire, WI.