| Summary: | 碩士 === 逢甲大學 === 土地管理所 === 93 === In the past years, remote sensing has been utilized to do the inventory of rice paddies by employ many technicians to manually interpret aerial photographs. This operation requires a large amount of photos and processing time. In this research, we propose an approach that integrates cultivating-field maps, multi-temporal SPOT images, and the spectral knowledge of rice growth to improve the efficiency of interpreting rice fields.
In the traditional supervised classification procedures, the selection of the training sites is done by human-computer interaction. In addition, the selected training sites cannot involve all the spectral variations of rice fields usually. The traditional method is time consuming and the classification accuracy is usually not very good. Therefore, it is necessary to develop a procedure to automatic select training sites to cover all the spectral variations of rice fields and further to improve the classification efficiency and accuracy.
The results showed that the supervised classification using the automatic selection of training sites is better than using the traditional human-computer interaction procedures. The bias induced by human while selecting training sites can be reduced by adopting automatic procedures.
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