Digital Image Processing III: Supervised Classification

Lab 6 - Due 7 PM on Oct 4, 2011 Student name: ______Geo5053/4093 - Remote Sensing, UTSA Page 1 of 6 Digital Image Processing III: Supervised Classification

Objective: Compute radiance for all bands. Stack individual bands together. Classify a study area using unsurprised and supervised classification methods. Calculate the flooded area.

Part I: Concepts and short questions

1. Summarize the differences between multispectral remote sensing and hyperspectral remote sensing

2. Choose four multispectral remote sensors and four hyperspectral remote sensors. Build a table to summarize their resolutions and coverages (spatial, spectral, radiometric, and temporal), sensor type (whiskbroom, pushbroom, how many pixels detector array, and how the pixels are arranged), and their major applications. You can use information from the text book or lecture notes, and you are encouraged to search the Internet. This table might take up a couple of pages.

3. Summarize the Terra, Aqua, and Aura satellites in terms of launch dates, major sensors carried on board, and purposes for each major sensor.

Part II: Lab

A. Preparation

Create a Lab6 subfolder under c:\UserData_ENVI\yourname\. Today we will use the atmospherically corrected image that you processed in Lab4: p27r40_July8_2002DOS.img. You do not need to copy this image from Lab4 to Lab6 - you can open the image from the Lab4 subfolder and save results to the Lab6 location.

Also, download the region of interest file from ftp://129.115.25.239/LRSGdata/studyarea.roi and save it your Lab6.

B. Background

From June 30 to July 8, 2002, a tropical storm hit the central and south-central parts of Texas. During these 8 days, the storm dumped as much as 35 inches of rain, with heaviest measurements in the Texas Hill Country northwest of San Antonio. The floods caused twelve deaths and damage to about 48,000 homes. Nearly 250 flood rescue calls were reported, more than 130 roads were closed, and thousands of homes and businesses lost electrical power and telephone service.

C. Calculate Radiance

In Lab 5, you calculated the radiance and reflectance for band 3 and band 4. In Lab 6, you will compute radiance for all of the other bands (1, 2, 5, 6, and 7) using the band math tool (see equation 1, table 1, and figure 1). Caution: for Band 6, use the Band 9 gain and offset values for the calculation. Output the results to the Lab6 folder and name them as Radiance-b1.img, Radiance- b2.img, Radiance-b5.img, Radiance-b6.img, Radiance-b7.img, respectively. Lab 6 - Due 7 PM on Oct 4, 2011 Student name: ______Geo5053/4093 - Remote Sensing, UTSA Page 2 of 6

(1) Lλ = ( Gain * DN ) + Offsett

where - gain corresponds to “Gain” in the header file - offset corresponds to “Bias” in the header file - Lλ units of radiance are W m-2 sr-1 um-1

Figure 1. Compute Radiance

D. Stack layers

After computing the radiance values, results are in individual image files. You need to stack them together into a single image file (BSQ format for multiple bands) for image classification.

First be sure all seven of the separate radiance files are open in ENVI. If the band 3 and band 4 radiance files are not open, you need to open them from your Lab5 subfolder. Next, select ENVI toolbar -> Basic Tools menu -> Layer Stacking to open the Layer Stacking Parameters popup window. Click Import file, select all 7 radiance band files, and then click OK. Lab 6 - Due 7 PM on Oct 4, 2011 Student name: ______Geo5053/4093 - Remote Sensing, UTSA Page 3 of 6

Back on the Layer Stacking Parameters popup window, make sure the Selected Fies for Layer Stacking section shows all bands in ascending order from top to bottom. If you need to click the Reorder Files button, the Reorder Files popup window works by dragging and dropping the files into the correct order. On the Layer Stacking Parameters popup window, keep all the default parameters the same and Output results to file in the Lab6 folder named as Radn_July8_2002.img. Click OK to stack all seven radiances files into the same file.

Later, we will use both unsupervised and supervised classification methods to classify this image.

Question 1: Compute basic statistics, histograms, and covariance for all seven band radiances. Remember to use the mask you created during Lab4, to exclude blank areas outside the image. Show the histograms, basic statistics, and covariance values for all bands in your report. Give your understanding of the following items: Statistics for each band, Relationship between bands, Relationship between Solar radiation, Earth emission, and sensor recordings of the radiances.

E. Unsupervised classification

The unsupervised classification method is based completely on pixel value statistics. Unsupervised classification will cluster pixels with similar statistics (mean, standard deviation, etc) into the same classes. ENVI has two types: K-Means and IsoData.

Question 2: Use lecture notes and ENVI help to summarize the K-Means and IsoData methods.

Select the ENVI toolbar -> Classification -> Unsupervised -> IsoData to open the Classification Input File popup window. Select your Radn_July8_2002.img input file and click the Spatial Subset button to open the Select Spatial Subset popup window. Lab 6 - Due 7 PM on Oct 4, 2011 Student name: ______Geo5053/4093 - Remote Sensing, UTSA Page 4 of 6

You can subset an image using various methods such as Image, Map, File, ROI/EVF. For Lab6, you will use ROI. Click the ROI/EVF button, then Open ROI/EVF file, and open the lab 6 ROI. (downloaded back at Step A from ftp://129.115.25.239/pub/LRSGdata/studyarea.roi). Click on the regions, click OK to return to the Select Spatial Subset popup window. Click OK again to return to the Classification Input File window. Click OK again to open the ISODATA Parameters popup.

Change your parameters to match the ones above. Save results to your Lab6 directory with a filename Isodata_July8_2002.img.

Isodata results will appear in the ENVI Available Band List. Open the classification image to a new grayscale display window. Enlarge the Isodata image window to maximum size to show the entire study area ROI, take a screen capture, and include this in your homework.

Link the Landsat RGB image and the Isodata image, and compare. Put your observations and your thinking in question 5 below.

F. Supervised Classification

For any supervised classification method to work, you must provide it with labeled training sites. These are specific pixel areas in the image that you decide should belong to a specific class... like crops, trees, bare dirt, water, clouds, etc.

You must decide how many classes will best define the image, and also decide which pixels in the image best represent the range of statistical values for each class. When selecting these pixels, the rule to follow is "Good Features make for Good Recognition". If you are careless with pixel selection, you may accidentally assign similar (or identical) pixel statistical values to multiple classes, which will produce unpredictable classification results. If you provide too few training pixels, the supervised classification routine might miss pixels that truthfully should be a member of that class, but because the pixel statistics are not close enough, you end up with many unclassified pixels. Lab 6 - Due 7 PM on Oct 4, 2011 Student name: ______Geo5053/4093 - Remote Sensing, UTSA Page 5 of 6

In ENVI, the ROI tool is used to define how many classes, and to define which pixels are the model for each class. For example, the image below shows training site classes, which I did for my study, saved into an ENVI ROI file.

The image below shows training site pixels for my study, with the different colors for each class. For each training class, I marked multiple places on the image to represent training pixel variations for the same class. You might decide to use different classes and different pixel areas. I recommend using recognizable names and colors for each class (blue for water, etc). Lab 6 - Due 7 PM on Oct 4, 2011 Student name: ______Geo5053/4093 - Remote Sensing, UTSA Page 6 of 6

G. Maximum Likelihood classification

There are many supervised classification method can be used. In Lab6, the Maximum Likelihood method will be used.

Question 3: Find the definition of Maximum Likelihood, repeat the definition here, then explain it in your own words.

Select ENVI Toolbar -> Classification menu -> Supervised -> Maximum Likelihood to open the Classification Input File popup window. Select the Radn_July8_2002.img as the input file, and spatially subset the image using the studyarea.roi file, just like for the unsupervised classification.

If your training class/pixel ROI file is already open in ENVI, click OK on the Classification Input File window to open the Maximum Liklihood Parameters window.

If your training class/pixel ROI file is NOT already open in ENVI: While you are still on the Select Spatial Subset window, click the "Open" button at the bottom to open an ROI file, and choose your training class ROI file. Then click OK on the Select Spatial Subset window and OK again on the Classification Input File window, which will open the Maximum Liklihood Parameters window.

On the Maximum Liklihood Parameters window, select all of your training classes, set the Probability Threshold to be a Single Value of 0.1, and set the Data Scale Factor to 255. Output results and rule images to memory, then click OK to start the supervised classification.

Maximum Liklihood results will appear in the ENVI Available Band List. Open the classification image to a new grayscale display window. Enlarge the image window to maximum size to show the entire study area ROI. Link it with the Landsat RGB742 image and compare. If you are not satisfied with your classification, you can modify the training classes, change the pixel members for each class, or change your probability threshold. Rerun the classification until the results seem good to you. Remember this is supervised classification.

Save results to your Lab6 directory with a filename Isodata_July8_2002.img. Enlarge the image window to maximum size to show the entire study area ROI, take a screen capture, and include this in your homework.

Link the Landsat RGB image and the Maximum Likelihood image, and compare. Put your observations and your thinking in question 5 below.

Question 4 : Find out what the probability threshold means. If using 0.1 as the threshold, what does this number mean? If you change the number to 0.3, 0.5, 0.9, what happens?

Question 5: Compare the two classified images (supervised and unsupervised) and state what you find. What are the similarities and differences between the resulting classes? What are the advantages or disadvantages of the two methods?

Question 6: Measure and report the flooded area (in km2) for the study area only. You can use any method you can think about.