Image Interpretation and Analysis of Aerial and Satellite Data

Part I. Image Interpretation

All remote sensing scientists require fundamental image interpretation skills to detect, identify, measure, and solve problems. Eleven fundamental image interpretation skills are listed in the table below.

Primary Elements	1. Black & White Tone
	2. Color
	3. Stereoscopic Parallax
Spatial arrangement of tone and color	4. Size
	5. Shape
	6. Texture
	7. Pattern
Based on analysis of primary elements	8. Height
	9. Shadow
Contextual elements	10. Site
	11. Association

As humans, we process profile views of the Earth all day long and are very adept at incorporating all of our knowledge for the interpretation of an image. Our minds might be able to recognize a feature on an image that a computer would have problems identifying due to our powerful visual processing capabilities and our experience. There has recently been a resurgence in the art and science of visual photointerpretation due to new digital remote sensing systems providing higher and higher spatial resolution imagery. For example, IRS-C (5 x 5 m) and IKONOS (1 x 1 m) panchromatic images are often photointerpreted and used as base maps for GIS projects. The demand for experienced photointerpreters will only increase as next-generation satellite systems proliferate.

1. Photointerpret the following images and identify the location for each image. Which fundamental image interpretation skills are you using to properly identify these locations?

A	B
C	D
E	F

2. Create a list describing the advantages and disadvantages of vertical vs. oblique photography.

3. Describe how f-stop and shutter speed work and why these are important considerations in capturing aerial photography.

4. Describe three important mission planning considerations when acquiring aerial photography.

5. How often and at what scale is NAPP data collected for each state? A 9 x 9 inch photo at this scale represents how much area on the ground? For what types of applications is this data useful?

6. Discuss some of the advantages/disadvantages of using satellite versus aircraft remotely sensed data.

Part II. Analysis of Aerial and Satellite Data

There is an abundance of digital data available in the remote sensing market today. This market is expected to grow substantially in the next few years with many new platforms being developed. This exercise will introduce you to some of the most common forms of airborne and satellite sensor data available today. Examples of airborne data include aerial photography data such as the National Aerial Photography Program (NAPP) and airborne multispectral scanning systems such as the Airborne Terrestrial Applications Sensor (ATLAS). Examples of common satellite based platforms include the multispectral scanning systems in Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) and the linear array sensors systems in SPOT Image XS (multispectral) High Resolution Visible (HRV).

In order to view the data for this exercise, we will be using ERDAS Imagine. Once you have successfully logged onto the system, click on the Start button in the lower left-hand corner of the screen. Go to Remote Sensing then click on ERDAS Imagine. After the Imagine toolbar is loaded, press the Viewer icon. Now you are ready to display an image. Move the cursor back to the imagine viewer and select the File dropdown menu with the left mouse button. In the file menu select the Open option and then slide to the menu which opens to the right and select the Raster option to get the corresponding menu. You can also type Ctrl R to access the open raster layer menu if the cursor is over the viewer or you can click on the viewer icon that looks like a manila folder that is half open. Additional viewers may be opened by clicking the viewer button on the IMAGINE icon panel.

In the Open File menu look for the list of files in the c:\Program Files\Imagine 8.6\Examples directory. These are example files that are included with the software. Feel free to browse these files at your convenience. The files that we will be using are located in the "Data" folder on your class network volume. Move to that directory to see the files we will be using for this exercise. To open a file, position the cursor over the file to be displayed and press the left mouse button (lmb). The file name should appear in a window above the file names. If you do not see a list of the files with a *.img extension, you are not looking in the correct directory, or the File Type has not been specified as IMAGINE Image (*.img).

Before clicking OK when opening an image, you will need to assign the spectral bands of the image to the color planes red, green, blue (RGB). These spectral band assignments will be given to you. Make sure that the Display option is set to True Color if you are displaying a multispectral image. You also have the option of making the image fit the viewer frame by clicking the small box next to Fit to Frame. Once you have specified all these option, you are ready to click OK. If an image is requiring less space in the imagine viewer (there are large black boarders on the sides) then you can resize the imagine viewer to use your screen "desktop" area more efficiently. This will become important in future exercises when many imagine viewers will need to be open at once. To remove an image displayed in the imagine viewer move to the File dropdown menu in that viewer and select it with the lmb, then find the Clear option and select it. You can also click on the "eraser" tool icon in the Viewer.

Additional information about each image can be found in the Tools drop down menu in the IMAGINE icon panel. Choose Image Information and wait for the Image Info dialog box to appear. Select Open in the File drop down menu and choose the image you are requesting information about. Once you have opened an image in your viewer, you can access the Quick View menu by positioning the cursor over the viewer window and pressing the right mouse button (rmb). Examine the options and move the cursor over the Fit Image to Window box and select it. The Quick View menu should then disappear. This will affect only the viewer you are currently using. For other viewers you will need to repeat the process. You can additionally use the View - Fit Image to Window command to achieve the same result.

** If you experience difficulties downloading the following zip files you can find the datasets for the exercises at ftp://gray.cla.sc.edu. 
     You need the username and password: both are rs.
     The files are under Exercise_RSE folder, Ex02.

	NAPP 1 x 1 m charleston_napp_1994-02-14.zip Color Infrared Composite RGB = Bands 3,2,1
	Landsat MSS 80 x 80 m south-florida_mss_1982-10-17.zip Color Infrared Composite RGB = Bands 4,2,1
	Landsat TM 30 x 30 m charleston_tm_1990-12-08.zip Color Infrared Composite RGB = Bands 4,3,2 Natural Color Composite RGB = Bands 3,2,1
	Landsat TM 30 x 30 m grandstrand_tm_1991-10-18.zip Color Infrared Composite RGB = Bands 4,2,1 Natural Color Composite RGB = Bands 3,2,1
	SPOT XS HRV 20 x 20 m SPOT Pan HRV 10 x 10 m marco-island_spot_1988-10-21.zip Color Infrared Composite RGB = Bands 4,3,2 Panchromatic RGB = Bands 1,1,1

Questions

7. Which Landsat platforms have the Multispectral Scanner (MSS), which have the Thematic Mapper (TM), and which have the Enhanced Thematic Mapper plus (ETM+)?

8. Study the differences between the MSS and TM bands. How are the TM bands an improvement over the MSS bands? Why do the TM bands offer improved vegetation discrimination over those of the MSS? How does Landsat 7 offer more in mapping capabilities?

9. Using the TM bands, how could one distinguish between clouds and snow?

10. For each of the following, choose one or more TM bands and explain why you think it should be used for the following feature discrimination:

Vegetation moisture content?

Soil moisture content?

Water body penetration?

Mineral and rock types?

11. Explain the primary difference between energy sensed with TM band 6, and the energy collected by the other sensors aboard TM.

12. In the color infrared composites, what do the red hues indicate? Be specific.

13. Which satellite has off-nadir viewing capabilities? How can this characteristic be useful in acquiring data?

14. Notice the difference between spatial resolution on the SPOT panchromatic (pan) and multispectral mode (1, 2, 3). Discuss some advantages/disadvantages of varying spatial resolutions and what platform would you use for each of the following applications.  Justify your responses.

1. Cadastral mapping
2. Precision agricultural
3. Urban and regional planning
4. Forestry inventory
5. Sea surface temperature mapping
6. Telecommunications (cellular antennae suitability)
7. 3-Dimensional visualization and simulation
8. Tourism and leisure

15. Complete the following table:

	Landsat MSS		Landsat TM		SPOT HRV
	Band	Micrometers	Band	Micrometers	Band	Micrometers
	4		1		1
	5		2		2
	6		3		3
	7		4		Pan
	8		5
			6
			7
IFOV at nadir
Quantization levels
Earth coverage
Altitude
Swath width

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