Students will also find the apparent arbitrary and non-repeatable nature of the training polygons frustrating (hence the development of automated classification schemes), but this is part of the exercise. Creating the training polygons is a little tricky and can be frustrating since erroneous polygons cannot be fixed, but must be completely removed and redrawn. Students will need some time to become familiar with the program. Image analysis is very memory-intensive, so the program will run faster and more effectively on machines with more RAM available. It runs on Windows and Macintosh computers. MultiSpec is a freeware program from Purdue University that is a very effective image-analysis package. Hybrid classification uses both techniques to make the process more efficient and accurate. ![]() These classes are statistically significant within the imagery, but may not represent actual surface features of interest. the ISODATA algorithm) to classify the image into a predetermined number of categories (classes). An unsupervised classification scheme uses spatial statistics (e.g. The "spectral fingerprints" of the identified features are then used to classify the rest of the image. In general, a supervised classification requires the manual identification of known surface features within the imagery and then using a statistical package to determine the spectral signature of the identified feature. ![]() Image classification is conducted in three modes: supervised, unsupervised, and hybrid. polygons) in order to compare with other data sets or to calculate spatial attributes (e.g. Additionally, the classified raster image can be converted to vector features (e.g. These may be used to identify vegetation types, anthropogenic structures, mineral resources, or transient changes in any of these properties. Image classification is a means to convert spectral raster data into a finite set of classifications that represent the surface types seen in the imagery.
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