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14 December 2015 Rice-planted area extraction from multi-temporal remote sensing images
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Proceedings Volume 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 98150M (2015) https://doi.org/10.1117/12.2205681
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
Abstract
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jinxiang Shen, Hong Zhang, and 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
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