Remote Sensing Lab 8

Spectral Signature Analysis

Goal

This remote sensing lab provided students with skills in measurement and interpretation of spectral signatures of Earth's features.  Students collected, graphed and analyzed spectral signatures from a Landsate ETM+ image of Eau Claire, Wisconsin.

Methods

A polygon was drawn using the Drawing tool in the spectral function in Erdas Image 2013 for each of the following features.

1.) Standing Water

2.) Moving Water

3.) Vegetation

4.) Riparian Vegetation

5.) Crops

6.) Urban Grass

7.) Dry Uncultivated Soil

8.) Moist Uncultivated Soil

9.) Rock

10.) Asphalt Highway

11.) Airport Runway

12.) Concrete Surface (Parking Lot)

Once a polygon had been drawn, the Supervised tool in the raster functions was used to activate the Signature Editor tool.  The spectral signature of the image was graphed using the Display Mean Plot Window tool in the Signature Editor interface.

Results

Each of the features were added to the graph for the analysis of spectral signatures (Figure 1).

Figure 1: Spectral Signatures of 12 Earth Features

The X Axis of the graph represents the reflective bands of the image.  The Y Axis represents the wavelength of the spectral signature.  The chart below displays the highest and lowest reflective bands for the 12 features collected.

Band 1 = Blue

Band 2 = Green

Band 3 = Red

Band 4 = NIR

Band 5 = MID

Band 6 = MID

Signature	Highest	Lowest
Moving Water	Blue (1)	Mid IR (7)
Vegetation	NIR (4)	Red (3)
Riparian Vegetation	NIR (4)	Red (3)
Crops	Mid IR (5)	NIR (4)
Urban Grass	Mid IR (5)	Green (2)
Dry Soil-Uncultivated	Mid IR (5)	Red (3)
Moist Soil-Uncultivated	NIR (4)	Mid IR (7)
Rock	Mid IR (5)	NIR (4)
Asphalt Highway	Blue (1)	NIR (4)
Airport Runway	Mid IR (5)	Green (2)
Concrete Surface-Parking Lot	Red (3)	NIR (4)

Figure 2: Highest & Lowest Reflective Bands

For All 12 Features

Discussion

The graphical representation of the spectral signatures helped students to recognize that each surface feature has its own unique spectral reflectance.  This knowledge provides remote sensing analysts with the ability to identify and map features that may be unfamiliar and can also determine what bands are most useful for the identification of features through spectral signatures.  Spectral signatures can also aid in the identification and classification of images by discrete land covers.

Remote Sensing Lab 7

Photogrammetry

Goal

This lab introduced students to photographic scale and how to calculate scale when various photographic measurements are available.  The lab also introduced basic concepts of photogrammetry, relief displacement and orthorectification.

Methodology

Scale

Figure 1: Western Eau Claire, Wisconsin

The ground distance between points A and points B (Figure 1) is 8,822.47 ft. Students measured the distance between points A and B on the JPEG photograph using a ruler to determine the scale of the photograph.

Ground Distance = 8,822.47 ft.

Measured Distance = 2.1 inches

8,822.47/2.1 = 4201.2

Scale: 1 inch = 4201.2 ft

Students also calculated the scale using focal length, focal height and terrain elevation.  The focal height of the photograph was 20,000 ft above sea level and the focal length was 152 feet.

152mm/20,000 ft – 796 ft

152mm/19,204 ft = 1mm/ x ft

Scale: 1 mm = 126.34 ft

Relief Displacement
Students determined the relief displacement of the smoke stack identified by the letter ‘A’ in Figure 2. The height of the camera was 3,980 ft and the scale of the photograph was 1:3,209.  To calculate the relief displacement, students measured the height of the smoke stack with a ruler to find its real world height.  The radial distance between the principle points of the photograph and the top of the smoke stack was also measured.

Radial Distance = 11 in

Tower Height = 1 in

D = (3,209 x 11)/3,980 ft

Relief Displacement: 8.87 in

Figure 2: Eau Claire, WI

Relief Displacement of smoke stack

Stereoscopy
An anaglyph image was created in this lab to show a 3D perspective view of the City of Eau Claire, Wisconsin.  A digital elevation model was added to an aerial image using the Terrain-Anaglyph tool in ERDAS Imagine 2013.  The vertical dimension of the image was exaggerating by 2 using this tool.

Elevation features of the city can be seen in the resulting image using Polaroid glasses (Figure 3).

Figure 3: Anaglyph Image of Eau Claire, Wisconsin

Orthorectification

Students performed orthorectification using ERDAS Image Lecia Photogrammetric Suite (LPS) for SPOT satellite images of Palm Springs, California.  The process of orthorectification is used to create a planimetrically true orthoimage.  Before an image can be orthorectified, ground control points must be collected to geometrically correct the image.  Once the image has been corrected, a panchromatic image is added to the block file and more ground control points are collected.  The number of GCPs collected for each image in this lab was 12.  12 Verticle Reference points were collected for the panchromatic image (Figure 4).

The next step was to perform automatic tie point collection to triangulate the images.  Figure 5 displays the triangulation report including the RMSE value of the ground control points.

Figure 4: GCPS & Verticle Reference Points
Collected for Orthorectification & Triangulation

Figure 5: Triangulation Report