You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
14 December 2015Rice-planted area extraction from multi-temporal remote sensing images
Rice-planted area and production monitoring has significance for governments to formulate some food related policy. Remote sensing has an obvious advantage for the rice monitoring. As for the rice-planted area, the special growth raw shows different feature in the remote sensing image. In this paper, the multi-temporal Landsat-8 OLI image of Menghun and Mengzhe town in Xishuangbanna autonomous prefecture where planting a large number of rice was used as the test data, the corresponding changes of the difference between NDVI and NDWI was used as the diagnostic feature, and the SAM classification approach was introduced to extract rice-planted area. The experiments shows that the approach could acquire more than 95% of the extraction accuracy.
The alert did not successfully save. Please try again later.
Jinxiang Shen, Hong Zhang, Yanmei Ma, "Rice-planted area extraction from multi-temporal remote sensing images," Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 98150M (14 December 2015); https://doi.org/10.1117/12.2205681