Download - GLIMS: Global Land Ice Measurements from Space

4.1. ALGORITHMS OF THE GLIMS - PROJECT 37ASTER image the debris covered parts of the glaciers are classied as rock in bothapproaches. In the Landsat image the glacier parts in the shadow are classied asrock in both attempts (20 and 31 clusters). The creation of a glacier mask can bequite dicult especially for the debris covered parts and cloud shadows.Figure 4.5a and 4.5b show the results for the Landsat 5 TM image for the wholeresearch area for the classes with 20 clusters and 31 clusters.a) b)Figure 4.5: Unsupervised classication of the Landsat 5 TM image a) with 20 clusters, b)with 31 clusters.4.1.6 Supervised ClassicationThe supervised classication takes a little more time than the unsupervised classicationbecause of the need of training areas. The rst step in the supervisedclassication is the selection of training areas. These training areas should be carefullyselected and accurate. They should also be distributed over the whole image.The 10 training areas in the Landsat image are: glacier tounge, water/lakes, towns,rock in sunlight, rock, rock/other in shadow, forest, snow on glacier, elds/grasslandand clear ice. In the southern ASTER image 9 training areas were: bright snow,snow, ice/tounge area, water, elds, forrest, rock, clouds and an extra glacier class.In the northern ASTER image 10 training areas have been selected: ice, snow/rn,forest, elds, clouds, grassland, rock, town, water and shadow. Seperate classes fordierent illuminations improved classication only in some parts of the images.The next step was the generation of signatures of the training areas with the programCSG. After that, the program SIGSEP was used to calculate the separabilitybetween the signatures. This showed that for the Landsat image the class glaciertounge and clean ice were the pair with the minimum separability for the southern