Remote sensing multispectral data semi-supervised labeling method based on self-learning

Abstract

A remote sensing multispectral data semi-supervised labeling method based on self-learning relates to the field of data labeling, and comprises the following steps: acquiring remote sensing multispectral image data in a research area, determining category information of a to-be-classified target, and performing image fusion on the remote sensing multispectral image data; selecting a sample plot in the research area, recording category information of a to-be-classified target in the sample plot, determining a pixel corresponding relation between the to-be-classified target in the sample plot and the fused remote sensing multispectral image data in combination with the fused remote sensing multispectral image data, and obtaining pixel category information of the fused remote sensing multispectral image data; taking the seed point data as initial labeled data, and removing redundant information between wavebands by using a principal component analysis method; using the processed data with the labels to construct a classification model by adopting a random forest algorithm; the method comprises the following steps: classifying unlabeled data, removing abnormal points, and obtaining a self-labeling data set after multiple iterations; the method has the advantages of low requirement on manual labeling data volume, high precision, fast classification speed and strong anti-noise capability.

Description

Technical field [0001] The present invention relates to the field of data labeling, and more particularly to a self-learning method based on self-learning remote sensing multi-spectral data semi-supervision labeling method. Background technique [0002] The data required by traditional geography, environmental science, and Earth science research are obtained by ground monitoring stations or field inspections, with long period of cycle, long time, and high cost. The remote sensing technology has greatly improved the above, with a short period of ocean satellite to acquire global images within a few hours, while land resource satellites can get global surface information in 10+ days. The application of remote sensing technology greatly shortens the time of data acquisition, reduces the cost of data acquisition, and the acquired multi-time data is of great significance for evolution. [0003] With a series of technologies, artificial intelligence has gradually entered the high-speed...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problems existing in the existing remote sensing data labeling methods, such as large manual labeling workload and difficulty in realizing large-scale data labeling, limited target types, and limited bands, the present invention provides a semi-supervised remote sensing multispectral data based on self-learning Labeling method

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