Drone's low-altitude remote-sensing image high-resolution landform classifying method based on characteristic fusion

Abstract

The invention discloses a drone's low-altitude remote-sensing image high-resolution landform classifying method based on characteristic fusion. The method comprises the following steps: selecting common and representative landforms from to-be-processed remote sensing images and using them as the training samples of the landforms; extracting the color characteristics and the texture characteristics from the training samples of each landform; fusing the color characteristics and the texture characteristics; using a classifying method to classify and learn the fused characteristics to obtain the classifying model for each landform; extracting and fusing the color characteristics and the texture characteristics of the low-altitude remote sensing images of the to-be-classified drones; and finally, based on the fused characteristics of the classifying objects and in combination with the classifying model of each obtained landform, using the classifiers to divide the classifying objects into a certain landform. Therefore, the classification of the drone's low-altitude remote sensing images is achieved. According to the method of the invention, it is possible to more effectively and more quickly to extract the verification characteristics so that the classification result becomes more accurate.

Description

technical field [0001] The invention relates to landform classification, specifically a method for high-resolution landform classification of low-altitude remote sensing images of drones based on fusion of color and texture features, and belongs to the technical field of landform classification of drone remote sensing images. Background technique [0002] UAV remote sensing is one of the development trends in the field of remote sensing. The UAV remote sensing system has the advantages of low operating cost and high task flexibility, and is an important tool for remote sensing data acquisition. With the maturity of technology and the needs of the civilian field, drones have gradually penetrated into various industries in the civilian field. In recent years, UAVs with different performances have been widely used in military battlefield reconnaissance and surveillance tasks and civilian research. According to the purpose, it can be divided into civil communication relay drone...

AI Technical Summary

Problems solved by technology

Its disadvantage is that it cannot describe the local distribution of colors in the image and the spatial position of each color, that is, it cannot describe a specific object or object in the image.

The SVM algorithm seeks the optimal classification surface between data based on statistics, and linearizes the nonlinear data by mapping it to the kernel function space, thereby simplifying the computational complexity and having a better classification effect; but how to choose Subspace and establishing a suitable model become the difficulty in the application of SVM

[0022] It can be seen from the introduction of the above prior art that for low-altitude remote sensing images of UAVs with large clutter interference, blurred target outlines, and inconspicuous features, the current feature extraction methods and classification methods have their own shortcomings. The complete features for identifying landforms cannot be effectively extracted. At the same time, several classification methods either have many influencing factors or have certain limitations, such as a large amount of calculation in the learning process and a long time-consuming, etc., both of which will affect the accuracy of the classification results.

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