GROWTH MONITORING OF WINTER WHEAT BASED ON OPTICAL REMOTE SENSING AND SAR DATA FUSION

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Year:
2016
Type of Publication:
In Proceedings
Keywords:
Winter wheat, Growth condition, Data fusion, Object-oriented classification
Authors:
Weiguo, Li
Abstract:
Multi-spectral remote sensing image in combination with radar image are conducive to the south area of extraction and crop growth monitoring. This study used three cities about Baoying, Gaoyou and Xinghua in the centre of Jiangsu Province in China as the study area, and made the Landsat / TM image and ERS/SAR image fusion in the winter wheat early jointing period, and then explored the remote sensing method of winter wheat planted area extraction. Based on the Optimum Index Factor (OIF) and spectral separability, selected bands 3-4-5 combination as the best band to classify. The traditional pixel-based classification results vulnerable to “the feature in different spectrum” and "foreign feature with the spectrum" effects. This study used object-oriented image classification approach with an object as a procession unit, and combined with a wealth of features in space, texture information for wheat area extraction, and then compared with pixel-based classification method (SVM classification) results. The results show the classification accuracy of SVM and object-oriented classification method is 78.59% and 94.16%, respectively. The object-oriented classification method can accurately extract the planting area of winter wheat, which is much better than the SVM classification method. Based on the extraction of winter wheat planting area, this study also monitored the winter wheat growth, and availably obtained the data and spatial distribution information of winter wheat in these counties. This method can give a technical support for the winter wheat planting area and growth information rapid access in the South China.
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