| Quickview | File - cola_tm7_2000-03-06.img |
|---|---|
 |
Landsat TM 7 Data
Band 1 = Blue (0.45-0.52) |
| Quickview | File - cola_tm7_2000-03-06.img |
|---|---|
|
|
Landsat TM 7 Data
Band 1 = Blue (0.45-0.52) |
 |
Create New Signature(s) from AOI |
 |
Replace Current Signature(s) with AOI |
 |
Merge Selected Signatures |
To gather the spectral signature of the sites you would like to place in the signature editor as training sites, you will need to use the AOI (Area Of Interest) tools. The AOI menu can be accessed through the current viewer's menu bar. In the AOI pull down menu you will be presented with many choices (AOI Styles changes the way the cursor styles look). The Tools and Commands options are important because they allow you to select the type of polygon, modify the polygon, etc. with which you want to encompass your AOI. The Seed Properties option is also important because it allows you to modify the limits of seed area growth by area and/or distance in addition to letting you select the Neighborhood selection criteria. We will be using the Neighborhood default setting which specifies that four pixels are to be searched, then only those pixels above, below, to the left, and to the right of the seed or any accepted pixels are considered contiguous. Under Geographic Constraints, the Area check box should be turned on to constrain the region area in pixels. Enter 500 into the Area number field and press Return. This will be the maximum number of pixels that will be in the AOI. Enter 10.00 in the Spectral Euclidean Distance number field. The pixels that are accepted in the AOI will be within this spectral distance from the mean of the seed pixel. Before closing the Seed Properties window, click on Options and make sure that the Include Island Polygons box is turned on in order to include nonadjacent polygons within the logical growth region.
To begin the process, you must select an area on the image using one of the AOI tools, such as the polygon or rectangle tool, or you can place a seed and grow a region using the Region Grow tool (looks like a magnifying glass in the AOI menu). Use whatever you need in that particular instance, just make sure you think you know what the area represents in terms of ground cover. In the viewer, zoom into an area where you want to select an AOI using the viewer's magnifier tool and then select the AOI polygon tool and draw a polygon around your chosen area (or you may plant a seed to grow). After the AOI is created, a bounding box surrounds the polygon or region, indicating that it is currently selected. While the area is selected, use the Create New Signature button to add the selected area into the Signature Editor. Now click inside the Signature Name column for the signature you just added and give it a name (use names like urban1, urban2, etc. to define your individual AOIs). You may also want to change the color in the Color column. You can use the Image Alarm tool under View in the Signature Editor to get a preview of the extent that the classes you have chosen represent the rest of the image. If you select the Image Alarm option a pop-up box titled Signature Alarm will open. In this box you can choose to indicate classes that overlap and the color that represents overlap. This can be useful if you are considering merging classes. The signature alarm will also, as mentioned, let you see the extent of each of the classes (you will need to do this anyway before you can see the overlap). Do this by selecting (highlighting) a class or a set of classes in the signature editor using the cursor. You can select the color you would like to represent the class as by clicking on the colored square with the right mouse button. Once you have made your selections click on the OK button in the signature alarm and let Imagine do its work. Using this tool, you can see what areas are covered and which are not using the classes you have selected (according to the rules of parallellepiped classification logic). For this lab, take at least three relatively distinct training sites for each of the following classes found in the Columbia scene:
When you are done generating the training sites for these 5 classes and you feel they are representative of the whole scene based on your use of the signature alarm, save the signature editor file using the Save As menu item under file in the signature editor menu.
|
Display Signature Mean Plot Window |
 |
Display Signature Histogram Window |
2) Explain why you think the mean plot and the separability cellarray seem to differ on the three most important bands in certain instances.
3) Which Distance Measure method did you choose and why? Which three bands appear to best separate the classes? (Use the results from the Signature Separability measures on this one)
4) Which land cover classes seem to be confused the most and why do think this is the case?
5) If you could combine or throw out certain signatures (training sites) to create sufficient separability between classes, which would you manipulate and how?
The Unsupervised Classification option is selected in the Classification menu under the Imagine Classifier icon. You will notice that the Unsupervised Classification dialog box states that it is an ISODATA unsupervised classification. The Iterative Self-Organizing Data Analysis Technique (ISODATA) is a widely used clustering algorithm and is different from the formerly used chain method because it makes a large number of passes through the remote sensing dataset, not just two passes. It uses the minimum spectral distance formula to form clusters. It begins with either arbitrary cluster means or means of an existing signature set, and each time the clustering repeats, the means of these clusters are shifted. The new cluster means are used for the next iteration.
The ISODATA utility repeats the clustering of the image until either a maximum number of iterations has been performed, or a maximum percentage of unchanged pixels has been reached between two iterations. Performing an unsupervised classification is simpler than a supervised classification, because the signatures are automatically generated by the ISODATA algorithm. However, as stated before, the analyst must have ground reference information and knowledge of the terrain, or useful ancillary data if this approach is to be successful.
To begin the unsupervised classification, click on the Classification icon and then select Unsupervised Classification... Fill in the input and output information in the Unsupervised Classification dialog box. Give both the Output Cluster Layer and Output Signature Set a similar name. Make sure that under Clustering Options the Initialize from Statistics box is on and set Number of Classes to 30. Set Maximum Iterations to 20 and leave the Convergence Threshold set to 0.950. Maximum Iterations is the number of times that the ISODATA utility will recluster the data. It prevents the utility from running too long, or from getting stuck in a cycle without reaching the convergence threshold. The convergence threshold is the maximum percentage of pixels whose cluster assignments can go unchanged between iterations. This prevents the ISODATA utility from running indefinitely. Leave everything else in its default state. When you have entered all the relevant information click OK to begin the process.
The next step is to open the Signature Editor (under the Classification menu) with the *.sig file you created in the unsupervised classification. Select all the clusters (they should all be highlighted in yellow). In the Signature Editor main menu select Feature and then in that pull-down menu select Objects This will display a Signature Objects dialog box that allows you to tell Imagine which viewer you want to receive the signature editor information about the clusters. In this case we want the viewer in which you have displayed your chosen feature space graphic. Select that viewer # in the Signature Objects space provided that represents this viewer. Select Plot Ellipses and Plot Means (or you can try the others if you like). Leave everything else in it's default state and click OK. Only selected clusters in the Signature Editor window will be drawn. More than likely your ellipses and means are multi-tonal in nature. If you would like them all to be white, red, green, etc... select all the classes in the Signature Editor dialog box using the mouse and change the color to the one you desire. Save the information as an Annotation Layer.
To analyze the content of the clusters, you should use a combination of techniques. The first should be to use the mean scatter plot to make some educated guess about the information in each cluster. You might want to label each of the 30 clusters on the scatter plot using the Label option in the Signature Objects dialog box so you know what cluster is containing which class. You will more than likely have to zoom in to get a better look at some of the clusters given the close proximity of clusters to each other. You should also have a viewer open with the original scene displayed. This will further help you identify the land cover class. If you are feeling adventurous, you can overlay your classified image on the original image and set all the clustered image's colors to transparent using the Raster Attribute Editor found under the Viewer menu (Raster - Attributes). Once you have set all classes to transparent then you can individually color (by making them opaque) particular classes and see where they are on the image. Another method may be to use the Utility - Swipe or the Utility - Fade tools in the Viewer by opening the classified image on top of the raw data (do not Clear Display after opening the first raw image). Regardless of how you choose to proceed you should not rely on any one particular method but a combination of methods and some common sense to arrive at a sound classification.
When you have decided upon the class breakdowns, use the Raster Attribute editor to assign class names and colors to the classification image. Create the same five classes you used in the supervised classification and place each of the 30 clusters into one of the classes by giving it the same color and class name as every other cluster in that class. When you satisfied with your unsupervised classification, finish the lab by doing the following:
7) Compose a one page description comparing the advantages and disadvantages between using a supervised and unsupervised classification approach. When would one approach be more appropriate than the other? Address the following issues: Accuracy? What could be causing some of the spectral overlap present between classes? What could you do to improve your results? Is there anything missing in your final image that was visually apparent in your Landsat scene? Are there any features you would like to see added to Imagine's classification procedures?
8) Create a new map composition comparing the completed supervised and unsupervised classification. Make sure the colors are the same between maps and somewhat appropriate to the class type. Include all appropriate cartographic elements. Remember, you are the map expert! (or you should be by now).
