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17 December 1999 Knowledge-based multisensoral and multitemporal approach for land use classification in rugged terrain using Landsat TM and ERS SAR
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Abstract
Land use has an important impact on the climatic and hydrological cycle. For modeling this impact detailed knowledge of the land use and land cover pattern is necessary. Optical remote sensing data are good information sources to derive land use classifications for large areas. But due to the fact that commonly used classification algorithms are solely based on the spectral information, this often leads to misclassifications, because different classes can show similar spectral signatures. This is especially true for areas where a high rate of cloudiness reduces the availability of data. These are often heterogeneous and rugged areas such as mountains and their forelands. Advanced knowledge-based classification approaches which integrate non-spectral geographical ancillary data (i.e. climatic and terrain data) can improve the classification accuracy drastically. Still the method fails if spatially distributed ancillary data is not available or show no influence on the land use structure. The major advantage of the approach described in this paper is that it uses data, which are solely based on remotely sensed images and is therefore independent from map sources. The lack of multitemporal satellite data is cleared by the synergistic use of ERS radar data and LANDSAT-TM optical data.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Roswitha Stolz, Gertrud Strasser, and Wolfram Mauser "Knowledge-based multisensoral and multitemporal approach for land use classification in rugged terrain using Landsat TM and ERS SAR", Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); https://doi.org/10.1117/12.373095
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