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《Journal of Geo-Information Science》 2017-01
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Research on the Method of Rice remote sensing Identification Based on Spectral Time-series Fitting in Southern China

SONG Panpan;DU Xin;WU Liangcai;WANG Hongyan;LI Qiangzi;WANG Na;College of surveying and mapping engineering, East China Institute of Technology;Institute of Remote Sensing and Digital Earth, CAS;University of Chinese Academy of Sciences;  
Food security is an important guarantee for the stable development of our country and the area of planting grain is the basis of food security, so the estimation of the area of planting grain is important. Remote sensing technology is an important method of estimating crop grain area at present. The classification accuracy is affected by cloud and mist, which cannot be avoided. To solve this problem, this study presented a method for recognizing rice based on GF-1 time-series image. With long time-series of GF-1 images, three indices of middleseason rice and late-season rice, namely near infrared band reflectance(NIR), red(R) band reflectance and the normalized difference vegetation index(NDVI) characteristics are extracted. Spectrum and the characteristic curve of vegetation index time-series are fitted. We analyzed the ratios of values of discrete near infrared band,red light band and NDVI of images of multiple temporal phases falling on both sides of the sensitive area of the fitting NIR, R and NDVI time-series curve of middle-season rice and late-season rice. This area can also be seen as the target area of rice identification features and only those reaching a certain proportion can be identified as certain type of rice. Under this condition, three kinds of situation should be considered comprehensively and voted to decide final classification results. The means of samples are used to fit the curve for each image. The outliers are eliminated from the ground samples in advance. Statistical analysis of ground samples defined target characteristics. The result indicated that:(1) Using polynomial fitting method based on least square principle to fit NIR, R, NDVI time series characteristic curve, fitting effect is better when fitting degree is 3 and it can satisfy the need of subsequent classification.(2) Different setting proportions led to different classification accuracy,and the overall accuracy is 95.76%, the user accuracy of middle- season rice and late- season is 95.97% and95.95% when the setting proportion is not less than 50%.(3) The method proposed in this study could solve the problem of the combination of complex phases, and significantly weaken the influence of cloud and fog on crop classification, especially in South China.
【Fund】: 国家自然科学基金面上项目(41571422);; 国家自然科学青年基金项目(41301497);; 中国科学院重点部署项目(KZZD-EW-08-05);; 高分辨率对地观测系统重大专项(00-Y30B15-9001-14/16-2)
【CateGory Index】: S511;S127
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