Crop area and phenology index extraction method

An extraction method and crop technology, applied in biological neural network models, image enhancement, instruments, etc., can solve problems such as overfitting, and achieve the effect of high accuracy and strong applicability

Active Publication Date: 2022-04-05
SOUTHWEST JIAOTONG UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

However, if the number of training samples exceeds the data range of the training set, it may lead to overfitting when modeling specific noise data

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  • Crop area and phenology index extraction method
  • Crop area and phenology index extraction method
  • Crop area and phenology index extraction method

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Embodiment Construction

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

[0049] Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative...

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Abstract

The invention discloses a crop area and phenological index extraction method, which comprises the following steps of: preprocessing an SAR (Synthetic Aperture Radar) image, including track correction, thermal noise removal, radiometric calibration, speckle filtering, terrain correction and decibel processing, so as to obtain a backscattering coefficient of crop VV / VH; constructing a feature vector according to a backscattering coefficient of crop VV / VH, proposing a full convolutional neural network model based on a time sequence, and meanwhile, performing crop planting area drawing according to time sequence SAR (Synthetic Aperture Radar) image data; in combination with an SAR-VV / VH method, a crop phenological index is extracted according to a regional crop growth cycle; compared with the existing random forest algorithm, the extraction method based on the full convolutional neural network has the advantages that the crop extraction precision is high, the spatial texture information of the crop plot can be relatively completely reserved, the purpose of accurate crop drawing is achieved, and the method has high applicability in crop extraction.

Description

technical field [0001] The invention relates to the technical field of agricultural remote sensing spatial information data measurement, in particular to a method for extracting crop area and phenological indicators. Background technique [0002] Nowadays, crop planting patterns play an important role in the dynamic monitoring of urban vegetation. Among them, the dynamic monitoring and yield estimation of crop growth has always been the core issue of agricultural remote sensing. Scholars at home and abroad have developed relatively mature crop recognition algorithms for spectral images. To sum up, it mainly includes the following categories: threshold method, support vector machine (SVM), decision tree (DT) and random forest (RF), etc., and the fully convolutional neural network is improved on the idea of ​​spectral neural network, and Combined with SAR images for crop identification and extraction. [0003] The advantages and disadvantages of using the above crop extracti...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06T5/00G06V10/774G06V10/82
Inventor 张瑞王婷庞嘉泰展润青李涛王晓文张伦宁
Owner SOUTHWEST JIAOTONG UNIV
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