This study involves stepwise application of Unconstrained Linear Mixer Model (ULMM) for sub-pixel classification of residential areas using Land sat 7 TM image. The image was geometrically and radiometrically corrected and spectral enhancement and classifications were done to determine the possible number of target classes. In the first step, five end-members were used as inputs and the pixels which were considered as well fit to ULMM were identified as outputs. The unidentified pixels were separated and taken to the second step with new end members. This method identified 52% of the mixed pixels were identified in the first phase and 6% in the second phase. 42% of the pixels were left as unidentified after the two steps. The pixels identified by ULMM were grouped into high and low density residential subclasses. The resulting image indicated very low RMS errors. However the percentages of pixels unidentified were high. The independent accuracy test carried out using census population density and the resulting image indicated a low relationship. A hyper-spectral imagery with finer spatial resolution may provide a better sub pixel classification. / Department of Geography
| Identifer | oai:union.ndltd.org:BSU/oai:cardinalscholar.bsu.edu:handle/187799 |
| Date | January 2004 |
| Creators | Abeykoon, Mahinda |
| Contributors | Rahman, Faiz |
| Source Sets | Ball State University |
| Detected Language | English |
| Format | vi, 88 leaves : ill. (some col.), maps (some col.) ; 28 cm. |
| Source | Virtual Press |
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