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Extracting winter wheat spatial distribution information from GF-2 image

  • role： First author第一作者
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

  • Email：15650099558@163.com
  • Introduction：宋德娟，1994年生,女,硕士研究生, 研究方向为遥感信息提取。E-mail:15650099558@163.com
SONG Dejuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

    Shandong Technology and Engineering Center for Digital Agriculture, Tai’an271018, China

  • Email：chming@sdau.edu.cn
  • Introduction：张承明，1971年生，男，副教授，研究方向为遥感信息提取。E-mail: chming@sdau.edu.cn
ZHANG Chengming12*,  
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

YANG Xiaoxia1,  
  • Affiliation：

    Shandong Provincal Climate Center, Jinan250031, China

LI Feng3,  
  • Affiliation：

    Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions， CMA, Yinchuan750002, China

HAN Yingjuan4,  
  • Affiliation：

    The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

GAO Shuai5,  
  • Affiliation：

    College of Information Science & Engineering, Shandong Agricultural University, Tai’an271018, China

DONG Haiyan1

Resümee

Winter wheat is one of the main food crops in China. The accurate spatial distribution information of winter wheat is crucial for yield estimation and food security. However, existing methods for extracting the spatial distribution information of winter wheat using a full convolutional neural network ignore the remote sensing imagery characteristics. The influence of differences among probabilities in the coded class probability vector on the judgment of pixel class attribution can lead to misclassification or missing points at the edge, which can affect the accuracy of the result. In this study, the RefineNet model is coupled with maximum a posteriori probability (MAP), and the WWRSE(Winter Wheat Remote Sensing Extraction) model is developed to create a method for extracting the spatial distribution information of winter wheat from a Gaofen-2 remote sensing image.The WWRSE uses the convolution RefineNet network structure to extract pixel features. An improved SOFTMAX model is used to obtain the pixel category probability vector. With the winter wheat category probability value and other categories as a poor probability vector, differences can be divided into small pixels and differences among large pixels according to the category probability vector. For large differences among pixels, the biggest probability category can be used directly as the pixel category. Small differences among pixels can be combined with the MAP model to determine the type of each pixel. Next, the model is trained using the stochastic gradient method, and the spatial distribution information of winter wheat is extracted from the remote sensing image using the successfully trained model.SegNet, DeepLab, and RefineNet were selected as comparison models. Experimental results showed that WWRSE accuracy improved by 4.2%, 7.6%, and 8.6%, compared with the comparison models. Moreover, overall extraction accuracy reached 93%, thereby indicating that the method proposed in this study has certain advantages in proposing the spatial information distribution of winter wheat.This method deeply explores the information contained in the class probability vector of the output of the full convolutional network and determines that the classification error is closely related to the small component difference in the class probability vector. Based on this finding, the classification result of the full convolution network is revised. The effectiveness and feasibility of the proposed method are proven by the experiments.

Schlüsselwort

GF-2;full convolutional neural networks;RefineNet;maximum aposteriori probability;winter wheat;spatial distribution;Zhangqiu

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