Large-scale transfer learning crop classification method and system based on phenological matching strategy

Abstract

The invention relates to the technical field of data identification, and provides a phenological matching strategy-based large-scale transfer learning crop classification method and system, a computer readable storage medium and electronic equipment. The method comprises the following steps: respectively carrying out phenological feature extraction on time series data of a reconstructed transfer learning area and time series data of a reconstructed target area to correspondingly obtain phenological features of the transfer learning area and phenological features of the target area; according to the phenological characteristics of the transfer learning area and the phenological characteristics of the target area, determining a crop phenological matching relationship between the transfer learning area and the target area; and based on a crop phenology matching relationship between the transfer learning area and the target area, extracting a training data set of the transfer learning area to train a phenology-matched crop classification model, and based on the phenology-matched crop classification model, classifying crops in the target area. In this way, available crop classification prediction results can be quickly obtained in the target area.

Description

technical field [0001] The present application relates to the technical field of data identification, and in particular to a large-scale transfer learning crop classification method, system, computer-readable storage medium and electronic equipment of a phenology matching strategy. Background technique [0002] With the development of technology, transfer learning has been widely used in more and more fields. In the field of crop classification and recognition, in the prior art, due to the obvious difference in the growth process of the crops in the transfer learning area and the target area, the classification accuracy of the trained crop classification model may be low after migrating to the target area, which cannot meet practical applications. need. [0003] Therefore, it is necessary to provide an improved technical solution for the above-mentioned deficiencies in the prior art. Contents of the invention [0004] The purpose of this application is to provide a large...

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Problems solved by technology

[0004] The purpose of this application is to provide a large-scale transfer learning crop classification method, system, computer-readable storage medium and electronic equipment of a phenology matching strategy, so as to solve or alleviate the problems in the above-mentioned prior art

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