Classify Geologic Terrains on Venus Classify Multi-Spectral Data Apply Multi-Variate Statistics

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Sophie Lizbeth Hall
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1 Classify Geologic Terrains on Venus Classify Multi-Spectral Data Apply Multi-Variate Statistics Operations What Do I Need? Recode Classify One of the main criteria of remote sensing image classification is detailed knowledge of the area to be classified. The user should know what classes do and do not exist within the study area and be able to identify several locations within the study area for each class they wish to identify in a multispectral or multivariate data set. To classify Magellan radar imagery of Venus you will need both high resolution reflectance images and low resolution multi-thematic images. The National Aeronautics and Space Administration (NASA) distributes these data sets on CD-ROM and via the internet ( The high resolution images are used to determine the location and variation of terrain type within an area of interest. The low resolution images are used to create a training map layer and to perform multi-band classification. Example In 1990, the NASA spacecraft Magellan began a four year radar mapping mission of the planet Venus. One of the mission objectives was to characterize the global landforms and tectonic features. The MFworks Classify operation can be used to classify geologic terrains on Venus using low and high resolution radar imagery. Tellus Regio is a km 2 patch of tesserated terrain that lies south east of Ishtar Terra, approximately halfway to Aphrodite Terra, dividing Leda Planitia and Niobe Planitia. This image displays the western two thirds of Tellus which appears bright in this radar image due to abundant folding HD-CVT-1

2 and faulting relative to the smooth, flat lava plains that surround it. The two white bands are data gaps. Four radar data sets are used in this example, three low resolution thematic images: radar emissivity, radar reflectivity, and root mean square (RMS) slope; and one set of high resolution reflectivity images. To classify this map layer manually, you would have to examine hundreds of high resolution map layers, determine which cells in the low resolution map layer are represented by the cells in the high resolution map layers and manually assign a class. The Classify operation allows you to examine a handful of representative high resolution map layers then assign classes to cells in the low resolution map layer that will act as training areas for the automatic classification of the rest of the cells in the low resolution map layer. The low resolution map layer has almost a quarter of a million cells to be classified so it is much more time efficient to classify a few hundred cells, then let MFworks classify the rest for you. Ground Truthing It is not possible to visit Venus in person to ground truth the geologic terrain classes, so high resolution images of Tellus Regio were examined to develop a geologic terrain classification scheme. Ten distinct terrain types were identified in the ground truthing stage: HD-CVT-2

3 ( 1) Clustered Conical Hills: ( 2) Fractured Plains: HD-CVT-3

4 ( 3) Smooth Plains: ( 4) Mixed Plateaux and Mountains 1: HD-CVT-4

5 ( 5) Mixed Plateaux and Mountains 2: ( 6) Embayed Blocks: ( 7) Highly Fractured: HD-CVT-5

6 ( 8) Mountainous: ( 9) Parallel Ridges: HD-CVT-6

7 (10) Complex Ridges: Developing the Training Map Layer Examine several high resolution images, such as those shown above, to determine the terrain type in the study area. Find the location of the high resolution image on one of the low resolution images such as Reflectivity or Elevation. Use the pencil tool, applying the technique outlined in the document Classify Multi-Spectral Data, to identify cells in the low resolution image that correspond to the terrain types that you identified in the high resolution images. Make sure that you create several training areas HD-CVT-7

8 for each terrain type. When you have finished defining the training areas, use the Recode operation to extract the training areas to a new map layer: Next, examine your low resolution maps layers to determine which ones will be used for the classification. NASA provides a radar reflectivity, radar wavelength emissivity, root mean square (RMS) slope, and elevation map layers. The geologic terrains are surface characteristics that are mainly independent of elevation, so only the following map layers are required for multi-radar band image classification: HD-CVT-8

9 Magellan space probe radar beam reflectivity: Radar wavelength natural surface emissivity: HD-CVT-9

10 And the root mean square (RMS) slope. This final image is an indicator of surface roughness. The higher the cell value (high values are assigned light tones in this image), the rougher the surface represented by the cell: You are now ready to classify the geologic terrains in the multi-layer radar data set. Use the Classify operation to perform a maximum likelihood classification of the multi-thematic images based on the classes that you specified in training map layer: If you were to perform this operation from the Script window the statement would be: "Classified Terrain" = Classify "Reflectivity" With "Emissivity" HD-CVT-10

11 With "RMSSlope" UsingÊ"TrainingMap"; The resulting map layer named Classified Terrain is comprised of cells that have been assigned to one of the classes specified in the training map layer based on the maximum likelihood that the cell is a member of one of the given classes. The colour scheme of the training map layer is used to colour the resultant map layer: HD-CVT-11

12 You can apply a more suitable colour scheme once you see the results of the classification: This example is based on the BachelorÕs Thesis: Lumsdon, M.P., Magellan Venus Radar Image Analysis and Mapping, Department of Geography, University of Western Ontario, London, Ontario, Canada, HD-CVT-12

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