Abstract
We present several methods for per-region land-cover classification based on distances on probability distributions and whole-region probabilities. We present results on using this method for locating forest areas in high-resolution aerial images with very high reliability, achieving more than 95% accuracy, using raw radiometric channels as well as derived color and texture features. Region boundaries are obtained from a multi-scale hierarchical segmentation or from a registration of cadastral maps.
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Trias-Sanz, R., Boldo, D. (2005). A High-Reliability, High-Resolution Method for Land Cover Classification Into Forest and Non-forest. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds) Image Analysis. SCIA 2005. Lecture Notes in Computer Science, vol 3540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499145_84
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DOI: https://doi.org/10.1007/11499145_84
Publisher Name: Springer, Berlin, Heidelberg
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