A cultivated crop type monitoring system based on progressive spatio-temporal spectral representation learning

By using a progressive spatiotemporal spectral representation learning system, the spectral, temporal, and spatial characteristics of cultivated crops are decoupled in stages, solving the problem that spectral and phenological changes are difficult to capture in existing technologies, and achieving more refined and stable identification of cultivated crop types.

CN122416243APending Publication Date: 2026-07-17SUZHOU IND PARK SURVEYING MAPPING & GEOINFORMATION CO LTD
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SUZHOU IND PARK SURVEYING MAPPING & GEOINFORMATION CO LTD
Filing Date
2026-03-27
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing deep learning models struggle to effectively capture spectral and phenological variations in arable land crops, leading to decreased classification accuracy. Furthermore, conventional convolutional networks struggle to simultaneously capture local textures and global structures, impacting the accuracy and reliability of arable land crop type identification.

Method used

A progressive spatiotemporal spectral representation learning system is adopted to decouple the spectral, temporal, and spatial features of multi-temporal multispectral remote sensing images in stages. Feature extraction is performed through a spectral sensing module, a temporal sensing module, and a spatial modeling module. Techniques such as NDVI, CNN encoding, Transformer blocks, and positional encoding are used to improve the quality of feature extraction.

Benefits of technology

It improved the classification accuracy of crop types in cultivated land and the quality of field-level identification, significantly enhancing the accuracy and reliability of crop type identification in cultivated land.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122416243A_ABST
    Figure CN122416243A_ABST
Patent Text Reader

Abstract

The application discloses a kind of progressive space-time spectrum characterization learning cultivated crop type monitoring systems, comprising;Remote sensing image acquisition module for obtaining the multi-temporal multispectral remote sensing image of target area on covering cultivated crop complete growing season;NDVI extraction module for calculating NDVI according to each phase ground surface real reflectivity time series image data;Spectrum perception module for reconstructing the time series spectral features of cultivated crop;Time series perception module for obtaining the time series aggregation features of cultivated crop based on the phenology curve of cultivated crop to the reconstructed time series spectral features of cultivated crop;Spatial modeling module for introducing position coding on the time series aggregation features of cultivated crop based on the field location of cultivated crop, spatial modeling is carried out, and the spatial features of cultivated crop are obtained;Classification module for generating the type distribution map of cultivated crop according to cultivated crop spatial features.
Need to check novelty before this filing date? Find Prior Art