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Crop classification based on two-dimensional representation and CNN model from remote sensing

  • role： First author第一作者
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zhang_q_k@163.com
  • Introduction：张乾坤，研究方向为农作物遥感分类。E-mail：zhang_q_k@163.com
ZHANG Qiankun12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：mengjh@radi.ac.cn
  • Introduction：蒙继华，研究方向为作物遥感监测及精准农业遥感应用。E-mail：mengjh@radi.ac.cn
MENG Jihua2*,  
  • Affiliation：

    College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China

REN Chao1

resumen

Crop planting area and its spatiotemporal distribution information are crucial for agricultural management, structural adjustment of the planting industry, national food security, and other fields. Remote sensing can extract crop planting information quickly. However, a large amount of ground survey information, expert knowledge, and manual correction operation after classification are needed to meet actual production needs. Using remote sensing and existing geographic information data for intelligent information extraction is the future development trend. The purpose of this study is to investigate the classification representation method of the deep learning model on the basis of the constraints of field parcel data. Multi-source heterogeneous data are linked by constructing representation. The method can identify the spectral differences of different crops in different phases and classify the crop types.The study area is in the eighth division of Xinjiang Production and Construction Corp. The platform of Google Earth is used to obtain all Landsat 8 images during the crop growth period of the research area in 2019. The image data and their quality assessment band on GEE are utilized to determine the statistical or estimated values of phases in the study area. Then, the average reflectivity of each band of the extracted block level is arranged in accordance with the time-phase sequence and wavelength, and a plot representation of the spectrum and time-phase two-dimensional properties is constructed. The construction of land plot representation realizes the connection of geographic information and remote sensing data and makes the application of deep learning in crop remote sensing classification possible. The completed plot representation is used to construct and train a Convolution Neural Network (CNN) model.A step-by-step optimization process is implemented to search for the best combination of super parameters and various types of layers. Construction of a relatively optimal CNN model is obtained, and classification of the constructed feature map is carried out based on this model to complete the classification of the research area in 2019. We obtain a crop distribution map whose resolution is much higher than that of the remote sensing image. Moreover, the overall accuracy of the CNN model reaches 93.04%, and the kappa coefficient is 91.09%. The results of nine kinds of crop classification are good. After thousands of rounds of data learning, the proposed method exhibits lower classification error fluctuation and higher stability than other machine learning algorithms.The research object is plot representation, which is the abstract expression of plot planting information. It can be used as the standard input of the deep learning model. Through the construction of plot representation, crop classification can be indirectly identified by remote sensing. The proposed method has outstanding application potential in land feature representation and crop classification and should be regarded as an optimal method for multi-temporal image classification tasks based on field parcel data. The method can be used as a reference in the application of deep learning in remote sensing.

palabra clave

remote sensing;crop classification;CNN;Feature map;Plot representation;multi-temporal image;Per-field

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