Object-oriented unmanned aerial vehicle high-resolution image coal fire region land cover classification method

Abstract

The invention relates to the field of high-resolution remote sensing image land classification, in particular to an object-oriented unmanned aerial vehicle high-resolution image coal fire region landcover classification method. The method comprises the main steps that on the basis of coal fire region high-resolution color images collected by an unmanned aerial vehicle, homogenous objects are divided through an image multi-resolution segmentation algorithm to construct an expert knowledge base adopting spectral signatures, shape features, textural features and height information as main parts,and high-precision classification of a coal fire region is achieved by means of a rule set and the most adjacent classification method, wherein the classification precision is much higher than that of a traditional pixel-based classification method. The requirement for complex coal ire region land cover precise classification is met.

Description

technical field [0001] The invention relates to the field of land classification of high-resolution remote sensing images, in particular to an object-oriented method for classifying land cover in coal fire areas with high-resolution images of drones. Background technique [0002] Compared with satellite and airborne remote sensing equipment, UAV, as a new type of remote sensing platform, has the advantages of short flight time, low flight altitude, flexible and convenient operation, low acquisition cost, and high resolution of acquired images. It is a traditional aerial photogrammetry method. It is a beneficial supplement to the system, and is widely used in the fields of topographic map production, disaster emergency management, confirmation of rural land rights, and urban 3D modeling. [1,2] . High-resolution UAV images contain more accurate spatial position information, richer geometric information and texture information, and easier to identify attribute information of g...

AI Technical Summary

Problems solved by technology

Dust pollution in mining areas and the impact of various surface disasters make the surface conditions of coal fire areas in mining areas extremely complicated. Some scholars have used traditional classification methods to carry out land cover classification studies in coal fire areas [8,9] , but it is very difficult to distinguish the ground objects in the coal fire area, the land cover classification is more difficult, and the classification accuracy is low

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