Cultivated land recognition method, system, device and storage medium based on distance between curves

Abstract

The embodiment of the present invention relates to the technical field of remote sensing technology, and discloses a cultivated land identification method, system, equipment and storage medium based on the distance between curves. The embodiment of the present invention first obtains the reference vegetation index NDVI time series curve of cultivated land crops and the first NDVI time series curve under the current area; Adjust the curve distance; weight the curve distance corresponding to the growth period in the curve distance to be adjusted to obtain the target curve distance; divide the pixels corresponding to the target curve distance within the preset value range into cultivated land pixels. It can be seen that the embodiment of the present invention can carry out weighting processing on the curve distance of the growth period, which can improve the final remote sensing classification accuracy for cultivated land, and solve the technical problem that the current remote sensing data classification technology is difficult to accurately extract cultivated land information.

Description

technical field [0001] The present invention relates to the technical field of remote sensing technology, in particular to a method, system, equipment and storage medium for identifying cultivated land based on the distance between curves. Background technique [0002] Land cover is one of the basic elements of the natural environment in which humans live, and changes in land cover have a profound impact on environmental quality, biodiversity, productivity and adaptability of terrestrial ecosystems. [0003] To gain a better understanding of land cover status, land cover classification methods can be applied. [0004] As far as the land cover classification method is concerned, the land cover classification method is a key technology to understand the distribution characteristics and regional structure of land cover. At the same time, remote sensing technology has become the main data source for land cover classification due to its advantages of low cost and high efficiency....

AI Technical Summary

Problems solved by technology

Therefore, when classifying land cover in tropical climate regions based on remote sensing data, many difficulties are often encountered

[0006] The difficulties encountered are as follows. First, in the cloudy and rainy tropical monsoon climate zone, the vegetation growth season is generally in the rainy season, which makes it difficult to obtain high-quality images in the rainy season; second, due to the abundant precipitation and abundant light and heat conditions in the rainy season, For cultivated land in the monsoon region, crops can be planted at any time during the rainy season, which leads to inconsistent planting times even for the same crop, and the multiple cropping index is relatively high.

These conditions make it difficult to obtain accurate land information

[0007] It can be seen that the current remote sensing data classification technology has the technical problem that it is difficult to accurately extract cultivated land information.

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