Laser-point cloud and image fusion data classification method based on multi-characteristic

Abstract

A laser-point cloud and image fusion data classification method based on multi-characteristic comprises the following steps: 1, data preprocessing: preprocessing aviation image and unmanned plane laser-point cloud data; 2, sample extraction: fully utilizing geometry characteristics of the point cloud data and spectrum characteristics of the aviation images so as to carry out sample extraction of various types; 3, fusion data classification based on multi-characteristic: using a vector description model to classify the sample data; 4, precision evaluation: evaluating the precision of the classified data. The method is complete in surface object extraction and high in classification precision. The method starts from the angle of the fusion image spectrum information, carries out data fusionaccording to application purposes and surface object classification demands, sets corresponding classification rules for classification of main surface objects, and builds a corresponding relation between classification types and classification characteristics, thus extracting complete surface object areas, and reducing misclassification phenomenon.

Description

technical field [0001] The invention relates to a data classification method, in particular to a multi-feature-based laser point cloud and image fusion data classification method. Background technique [0002] The data source of traditional ground object classification mainly comes from remote sensing images of aerospace photography, which are classified according to the spectrum, texture and other characteristics of the image, but it is easily affected by time, climate and weather, especially in recent years, the serious decline in air quality in my country has made it difficult meet the needs of practical applications. LiDAR technology (Light Detection And Ranging, LiDAR), as a new type of active remote sensing technology, can quickly, accurately and timely obtain remote sensing data on the earth's surface, making it useful in emergency response to major natural disasters, low-altitude large-scale high-precision measurement In the case of map, urban planning or 3D model re...

AI Technical Summary

Problems solved by technology

[0002] The data source of traditional ground object classification mainly comes from remote sensing images of aerospace photography, which are classified according to the spectrum, texture and other characteristics of the image, but it is easily affected by time, climate and weather, especially in recent years, the serious decline in air quality in my country has made it difficult Meet the needs of practical applications

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