A high-precision land cover classification method based on high-resolution satellite imagery

Abstract

A land cover classification method of the present invention comprises the steps as follows: (1) determine the patch regions with similar properties of domestic remote sensing images so as to perform reasonable image segmentation; , geometric characteristics, and calculate image features; (3) use historical land use data and feature spectral library, combined with (2) image features, automatically obtain ground feature samples; (4) use (3) ground feature samples as information entropy, Use decision tree and boosting technology to extract various ground features; (5) Calculate the area of ​​various ground features, merge and eliminate the spots according to the set value, and finally obtain the classification result. The invention solves the problems of land cover classification misclassification and many broken spots, improves the operation efficiency by 50%, and improves the classification accuracy to 90%.

Description

technical field [0001] The invention relates to an object-oriented high-precision land cover classification method based on domestic high-resolution satellite images, which is suitable for land cover classification of multi-spectral images of domestic remote sensing satellites without cloud coverage, and belongs to the field of remote sensing information processing. Background technique [0002] With the development of the economy, the damage to the global environment is becoming more and more serious. Problems such as droughts and floods, illegal occupation of agricultural land, and deforestation frequently occur and seriously affect people's lives, social and economic development, and social stability. Using remote sensing information to quickly and scientifically , Accurately classify and evaluate the land cover, grasp the situation of regional land cover in a timely manner, and take corresponding countermeasures in time, which play an important role in the rational planni...

AI Technical Summary

Problems solved by technology

This method requires more manual intervention, more subjective judgments, and the results are more fragmented

[0007] In general, the above methods have the following disadvantages: (1) The method is relatively primitive and the extraction accuracy is low; (2) The extraction process requires auxiliary manual intervention, and even requires on-site measurement and manual sketching, which is time-consuming and laborious ; (3) For different remote sensing satellite multispectral data, different calculation models and thresholds are required, and the universality is low; (4) For high-resolution images, the classification result data of the pixel-oriented method is relatively fragmented, which is not suitable for practical use

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