Plastic-mulched Farmland Extraction with Multi-source Satellite Data

doi:10.13474/j.cnki.11-2246.2018.0215

Abstract

Abstract: The rapid development of agricultural productivity has been driven by the extensive use of plastic-mulched farmland (PMF).With the increase of PMF usage year by year,its influence on environment cannot be ignored.Remote sensing image classification is an important method for PMF monitoring.Based on FSO feature selection and object-oriented random forest classification method,several sets of experimental schemes were designed using the characteristics of spectrum,geometry and texture,etc.PMF extraction was carried out in the middle area of Gansu province with the sharpening image of ZY-3 and Landsat 8 OLI data.The experimental results show that the extraction accuracy of multi-feature sharpening method is higher than that of single-feature extraction method. The overall accuracy and Kappa coefficients of the experimental scheme using FSO feature optimization are 90.2% and 0.877,respectively,which is obviously higher than other schemes.It indicates that the feature interference is decreased by the feature optimization method while feature space dimension is reduced,and the classification accuracy is improved.

Key words: plastic-mulched farmland, object-oriented, feature selection, random forest

CLC Number:

P237

LI Jiayu, WANG Huabin, WANG Guanghui, ZHAI Haoran, HAN Min, CHENG Qian. Plastic-mulched Farmland Extraction with Multi-source Satellite Data[J]. 测绘通报, 2018, 0(7): 78-82.

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