Remote Sensing Techniques Using ArcView 3.2 and ERDAS Imagine 8.4 CEE 4984/5984 GPS/RS Applications in CEE May 1, 2001 Mark Dougherty Colin Kraucunas Jen Verwest Table of Contents Page Table of Contents……………………………………………………………... 1 Project Statement…………………...………………...…………….……….. 2 Tutorial 1: Mosaicking images in ERDAS Imagine 8.4………..……….……. 3 Tutorial 2: Creating image subsets in ERDAS Imagine 8.4….……...…..…… 6 Tutorial 3: Stacking images in ERDAS Imagine 8.4…......…………………. 11 Tutorial 4: Supervised classification in ERDAS Imagine 8.4….…………….. 15 Tutorial 5: Unsupervised classification in ERDAS Imagine 8.4……………... 22 Tutorial 6: Vegetative mapping in ArcView 3.2………......…………………. 30 1 Project statement The purpose of this document is to present the techniques needed for basic analysis of remotely sensed images, including mosaicking, subsetting, stacking, unsupervised and supervised classification, and vegetative mapping. The document is written as a set of six tutorials with step-by-step instructions. It is assumed that the student has access to a working version of both ERDAS Imagine 8.4 and ArcView 3.2, and has a basic familiarity with both software packages. It is recommended that the two data sets provided with this document be used to complete the tutorials. The data sets include both SPOT and Landsat images for the New River Valley, located in southwestern Virginia. SPOT image files 37080SE.BIL and 37080SW.BIL are located in folders of the same name, as shown in the figure below. These two SPOT images, when mosaicked together, provide remotely sensed coverage for the entire New River Valley. The remaining files, shown below as nrvband1.img through nrvband5.img, represent the five bands of light reflectance (visible through midinfrared) captured by Landsat instrumentation. The Landsat images, when stacked together, provide remotely sensed data coverage for the same geographic area as the SPOT images. 2 Tutorial 1: Mosaicking images in ERDAS Imagine 8. Please note that the images must be in *. When the copying image process is finished. It is often helpful to mosaic two images together. If the images are in another format. Click on Add… 5. select the file. Click Add… to add images. Select the correct file type. hit OK 7.img files.4 This tutorial will help you join two images together in a process called mosaicking. click the Viewer button to open a new viewer. especially when the two joined images result in a complete picture of an area being analyzed. The Add Images for Mosaic window will open. 2. click Close. open them in the viewer and save them as *. as shown below. Click on Mosaic Images… 3. Select a path and filename to save the image and hit OK 6. Find and select the second image and click Add. The Mosaic Tool Window will open. Choose Edit>Image List… 4. In ERDAS Imagine.img format to complete the steps outlined in this tutorial. and hit OK 4. Complete the following steps to convert images to *. Close the viewer Complete the following steps to mosaic two IMAGINE images (*. Click the Open Layer button or choose File->Open->AOI Layer 3.img files: 1. 3 . then click Close on the Mosaic Image List window. The Mosaic Image List will open. Choose File->Save->Top Layer As… 5. Click on the DataPrep button on the ERDAS Imagine Toolbar 2.img): 1. Find and select the first image file and click Add. When finished. as shown at right. choose Edit->Image Matching… 7. Choose “For All Images” as the Matching Method and click OK. To edit the overlap area options. 8.6. The outlines of your two files should be shown in the Mosaic Tool window. choose Edit->Set Overlap Function… 9. The Matching Options window shown at right will open. To edit the mosaicking options. Choose the “Feather” function and click Close 4 . The Set Overlap Function window shown at far right will open. 15. choose File->Save to save the mosaic options as a *. select your image file and click OK 18. Your mosaicked image is displayed in the viewer.img). 13. You are now ready to mosaic the images. then click OK 12.10. Close the Mosaic Tool window. Your mosaicked image is ready for viewing. Choose the path and filename and click OK 14. click OK in the progress dialog box as shown below.mos file. Change the file type to IMAGINE Image (*. Choose Process->Run Mosaic… 11. When it is complete. Click on Viewer in the ERDAS Imagine Toolbar to open a new viewer 16. ERDAS Imagine will run the Mosaic procedure. In the Mosaic Tool window. 5 . Specify an output path and filename for the combined image. In the viewer. which may take a few minutes. choose File->Open->AOI Layer… or hit the Open Layer button 17. 2. Rectangle tool 3. Subsetting is the process of “cropping” or cutting out a portion of an image for further processing. This can be done from the viewer menu by selecting AOI -> Tools.img files can be used). you will create an image subset using the following steps. In this tutorial. 6 . 1.4 Subsetting an image can be useful when working with large images. This will automatically set the extents of the Inquire Box to match those of the AOI. create an AOI (area of interest) rectangle around the desired subset area. Hit Apply.Tutorial 2: Creating image subsets in ERDAS Imagine 8. Open the image in a viewer (any one of the nrvband*. Using the rectangle selection tool. Choose Utility -> Inquire Box ->Fit to AOI. As shown at right. select Data Prep-> Subset Image. Hit OK to create a new subset image. A progress bar. shown below.] 7 . When you repeat step 3. This sets the extents of the subset to match those of the inquire box selected in step 3. will display when the new subset image is created. 7. you will automatically fit the coordinates of the preselected AOI to the current image. then specify the output file name and location. Hit OK to accept the new subset image.4. select the input file using the browse button. This will give you the following dialog box: 5. Next. 8. [Note: For each image.img). then select the rectangular AOI from step 2. Hit the “From Inquire Box” button. 9. which will be saved to the location specified in step 6. Repeat steps 1 – 8 for each of the four remaining band files (nrvband*. 6. first load. The next step in the subsetting process is to combine three band images into one multispectral image.” This will set the coordinates of the new image to match those of the subsets previously created. select the output file browse button and specify a path and output file for the combined image. The following dialog box appears. The cell size fields should be changed to 28.10. 11. as shown at right. [Note: you can find this information under Utilities-> Layer Info. 12. Begin by selecting Data Prep-> Create New Image.50 to match those of the subset images. From the Create File dialog box.] 8 . Hit “From Inquire Box. below. Note: The image combination shown above represents the default band combinations representing visible blue. 9 . and red bandwidths. When the process has completed. therefore layer settings can be left at one. The next step is to open the new image created in step 10. hit OK. hit OK. The image will appear all white because it does not yet have raster information in it. select Raster -> Band Combinations. Use the browse buttons to select the appropriate band subset files for each band. Each subset image only has one layer. The dialog box. will appear. 16. 15. Hit apply and you should have a color image similar to the one shown at right.13. To do this. green. A progress bar will appear. When all values in the Create File dialog box for Date Type and Output Options are as shown above (step 10). 14. then select a name and output location. The resulting image. shown below. Depending on the types of analysis you are doing.17. is widely used in the classification of vegetation vs. it may be necessary to re-order the band combinations to obtain the correct appearance. while bare earth or asphalt has very little to zero infra-red or red reflectance. select File -> View to Image File. Note: The image combination shown above represents the default band combinations representing visible green and red bandwidths. This is done by once again selecting Raster -> Band Combinations. In order to save your image with these settings. 10 . bare surfaces because the healthy vegetation has a very high infra-red reflectance. as well as near infra-red. 18. I n the Utilities menu. select Layer Stack. select the first layer for the Input File by selecting the browse button. Navigate to the desired folder. 21. The following steps show how to stack images. select the Utilities. 19. This will allow for different combinations of RGB to be shown in the view. and select the image that will be Layer 1 in the new image. 11 . 22.Tutorial 3: Stacking images in ERDAS Imagine 8. In ERDAS. The Layer Selection and Stacking dialog box will appear.4 In order to analyze remotely sensed images. 23. In the Layer Selection and Stacking dialog box. the different images representing different bands must be stacked. click on the Interpreter button on the ERDAS Imagine Toolbar 20. Click the Add button to create this file as Layer 1. When the Image Interpreter dialog box appears. 25. Continue to select the input files in order and click Add.24. create an Output File by selecting the browse button and navigate to the desired folder. and click Ok. 26. 12 . The files will become the layers of the new image. Once all the files are added. Verify the remaining options. Name the file and hit Ok. the Green layer is red. choose Raster then Band Combinations in the Viewer dialog box. 28. Open a new Viewer and open the newly created raster image. The Red layer is near infrared. When the Modeler dialog box is complete. To change the layer being displayed. click Ok. The selection shown below displays a combination commonly used for land use and vegetative mapping. 29. Change the layers that are displayed for the respective colors in order to get the desired bands visible. 30. 13 .27. and the Blue layer is green. Hit Ok to view the image. below.31. 14 . Hit Ok. Open the raster image with the different bands stacked in layers as created in tutorial 3. shown on following page.4 Image interpretation is the most important skill to be learned before producing accurate land use maps from remotely sensed data. Facts about the area. This band combination and color selection make identification of bare surfaces easily distinguishable from healthy vegetation. and red. On the Viewer menu. and near infra-red (4) band are represented by blue. 20. is a common band combination used to evaluate land use and vegetation. and experience in image interpretation permit pixels with specific characteristics to be selected for a better classification of the image. respectively. and the Blue Layer is Green (Layer 2). knowledge about aerial photography. red (band 3). In this image. green (band 2). choose Raster and select Band Combinations from the list. the Green Layer is Red (Layer 3). The image that appears. 19. change the layers so that the Red Layer is Near Infrared (Layer 4). 21. 15 . Supervised classification allows the user to define the training data (or signature) that tells the software what types of pixels to select for certain land use. 22. Through experience. supervised classification becomes easier and more accurate.Tutorial 4: Supervised classification in ERDAS Imagine 8. When the Band Combinations dialog box appears. green. In the Classification menu. select the Classifier button. select the polygon tool to create an AOI. 26.23. In the AOI Tools dialog box. The signature dialog box will appear with a new file opened to begin defining training data. 25. 16 . The signature editor allows the user to select areas of interest (AOI) to be used as training samples to categorize the photograph. In the Viewer menu. select Signature Editor. On the main ERDAS menu. 24. select AOI and then choose Tools. 28.27. and draw a polygon around a specific region to be used for training data. Zoom into an area to be classified. 17 . hit the Select New Signature(s) from AOI button. With the AOI still selected. In this example. based on knowledge of the area. water by blue. 30. The resulting image classifications will distinguish deciduous from coniferous trees using different shades of green. Browse to the desired folder. Continue to select more signatures until all desired land uses or areas are selected. With careful pixel selection.29. 31. bare soil is considered to be bare soil fields (as opposed to urbanized impervious areas such as asphalt). Urbanized bare areas will be represented by red. select File then Save As. Name the file and click Ok. 18 . In the Signature Editor dialog box. Change the color to be displayed that is defined by this signature by clicking in the color field and selecting a new color. and agriculture by yellow. bare soil can be distinguished from urbanized bare areas. Change the signature name by clicking in the field and entering a more descriptive name. 32. 19 . a supervised classification can be performed. The Supervised Classification dialog box will appear. Close the Signature Editor dialog box by selecting File then Close. select the Supervised Classification button. In the Classification dialog box. 33. Now that the Signature File has been created to select the different classifications. To select the Input Signature File. and urban (red). bare soil (pink). click Ok. 36. To select the Input Raster File. Name the file and hit Ok. Select the Signature File previously created. To create a Classified File. 37.34. When the Status dialog box is complete. 38. hit the browse button and move to the preferred folder. hit the browse button and navigate to the folder where files are being saved. 20 . Verify the other settings below and click OK. 39. Open the newly classified file to observe the classifications and verify the signature file. hit the browse button and navigate to the desired folder. Water is shown in blue. Select the file to be classified that is open in the viewer. 35. followed by agriculture (yellow). Most of the land use is deciduous trees (light green). merge . 41. and delete (in 43. select File then Open. If the signature file needs to be edited. Save the redefined signature file and repeat steps 15 through 21. Image interpretation is the most important skill to be learned before producing accurate land use maps. Edit the signature file by using the add the edit menu) options. Navigate to the desired file and open the previously created signature file. 42. open the Signature Editor by clicking on it in the Classification dialog box. In the Signature Editor dialog box. 21 .40. . Through experience. supervised classification becomes easier and more accurate. replace . simplicity comes at a cost.4 Performing an unsupervised classification. The resulting classification has less discerning abilty than a supervised classification due to the lack of training data supplied to the clustering algorithm. On the main ERDAS menu. In this tutorial. In unsupervised classification. which will open the Classification menu. the signatures are automatically generated by an algorithm named ISODATA. 22 . select the Classifier button. The Unsupervised Classification dialog box. On the Classification menu. 45. select Unsupervised Classification. 44. shown at right.Tutorial 5: Unsupervised classification in ERDAS Imagine 8. Unfortunately. you will perform an unsupervised classification using the following steps. shown on the next page. is simpler than a supervised classification. 46. covered in this tutorial. will appear. give the target destination and filename of the output file. Under Output Cluster Layer. 48.950. shown below. Click OK to begin the classification process. as shown below.47. Maximum Iterations = 24 and Convergence Threshold = 0. Hit OK when the process is 100% complete. Click the Output Signature Set to disable the Output Signature Set filename box (you will not be creating a signature set as you did with supervised classification). Under Input Raster File. Next set the clustering options as follows. will alert you as to progress. place the name of the file and file location to classify. 50. 49. 23 . The job status dialog box. Put 5 for number of classes. 24 . After the classification process is complete (yes. Change the Layers to Colors 4. and 2. 53. you will want to evaluate and test the accuracy of the classification. To do this. Change the colors displayed to match those used in tutorial 4 by selecting from the Viewer menu bar Raster ? Band Combinations. A good way to evaluate the results of the unsupervised classification is to overlay the original image data with your *_isodata. 52. The image at right should appear in your viewer.img file. The following familiar image should appear. You may also want to reclassify your image using a different number of classes with a different number of iterations.3. from the Viewer menu bar open the original image with File ? Open ? Raster Layer. that is all there is to unsupervised classification processing).51. Let’s see what the classified image looks like. above. 25 . click the Raster Options tab at the top of the Select Layer to Add dialog. After loading the *_isodata. 55. select Raster ? Attributes then Edit? Column Properties. In order to add the new image without clearing the original image.54. You will rearrange the columns of the following editor box as follows.img file previously. and clear the Clear Display box.img image. Open a new file dialog box as described in step 9. Select the directory where you saved the *_isodata. The next step is to overlay the classified image over the original. as shown below. 56. 57. Select the column headings one at a time as shown and hit the Up key to rearrange the headings. hit OK. as shown. 26 . When complete. below will appear with columns as below. The Raster Attribute Editor box. until you are ready to add colors. does not seem appropriate for water. and note the change in the raster image displayed. select the Opacity column to highlight all the values in blue (shown above). The appearance of the final drawing will depend on the color combination and number of classifications you choose. at right. With unsupervised classification. Continue editing the five classes one at a time. red. The resulting drawing and legend may look like the following. one at a time. which is obviously displayed in the image. This color and heading can easily be edited to blue by clicking on the cell. For example. adding colors of your choice to represent what you think to be specific features. 60. 59. Note that the Classes have not been given names. yet. Select Edit ? Formula and place a zero in the Formula box function above as a value for all cells in the column. This makes the newly classified image effectively transparent. respectivel.58. Change the color and opacity in Class 1 to red and 1. the first color chosen above. it is often necessary to do 27 . Next. several attempts at the unsupervised classification may be desirable to achieve a land classification that is understandable. The color scheme is the same. 28 . A somewhat more intuitive image display is presented below.ground truthing after the classification is complete. but the image has been re-classified using only four classes. Be aware that there are bound to be trade-offs in the selection of classes that will depend upon the use being made of the data and the land use being categorized. As stated previously. 61. Although the map below may be easier to interpret than the one above. it likely will have somewhat less discriminatory detail. specifically the Blend. We leave it to you to experiment with these handy tools as you gain more experience in your classification skills. or swipe. or flicker the upper-most image alternately with the lower image within the View. which you can now view in periodic “swipes” or “flickers” or “blends” to help evaluate the types of land cover beneath your classified image. Swipe. and Flicker commands. Enjoy! 29 . Swipe. Recall from step 11 that you overlaid the classified image on top of the original. and Flicker commands. Each of these commands will bring a control box that will either blend. A useful aid to evaluating unsupervised classifications is through the use of the Utility menu on the viewer menu. 63.62. The following are the three control boxes that are activated by the Blend. 68. Open the image by clicking the Add Theme button. In ArcView.2. In order to distinguish healthy vegetation from other reflective sources.Tutorial 6: Vegetative mapping in ArcView 3. visible red. a Normalized Difference Vegetation Index (NDVI) is calculated in ArcView using the formula NDVI = (IR-R) / (IR+R). add the Image Analysis extension by selecting File then Extensions. A single band theme in grayscale is created that highlights vegetation. the visible red is chosen. Check Image Analysis and click Ok. From the visible red and near infrared layer. 30 . which contrasts vegetation from bare soil. 64. especially healthy vegetation. Change the Data Source to Image Analysis Data Source and Navigate to the desired folder. 66. In order to do vegetative mapping. 65. visible red and near infrared. and man-made features. two bands are needed. These bands are chosen because vegetation. is very reflective in the near infrared range and it provides good contrast with water. Open a new view to add the raster image for the vegetation mapping with the green. where IR is infrared and R is visible red. and near infrared bands created in tutorial 2. The following tutorial shows how to quickly build an NDVI vegetative mapping image in ArcView 3. rocky surface. Click Ok.2 Vegetative mapping finds areas of healthy vegetation as well as stressed vegetation from a remotely sensed image. 67. Click Apply and close the Legend Editor dialog box.69. the Green Band to the Green Layer. 31 . Click the check box next to the theme to draw it. Double-click the theme to bring up the Legend Editor. and the Blue Band to the Blue Layer. Change the Red Band to the Red Layer. 70. The image is displayed in the commonly used form for vegetative mapping.71. 32 . The Vegetative Index dialog box appears. Change the Near Infrared Layer and Visible Red Layer to Layer_Red and Layer_Green. or according to how the image was generated in ERDAS. 74. 73. select Image Analysis the Vegetative Index.72. respectively. Click Ok. 33 . Click the check box next to the NDVI theme to draw it. In the Main menu. 78. The bright areas represent areas of vegetation and the dark areas represent water. and bare soil. 34 . Navigate to the desired folder. and name the image. In the View menu. urban. and click Ok. 76. Choose IMAGINE Image as the file type to be able to use in ERDAS.75. 77. select Theme then Save Image As. Select No to add the image as a theme to the view. Verify that the file type is the desired format. Save the image by selecting the NDVI theme.