Deep learning based raft cultivation remote sensing image scene labeling method

Abstract

The invention relates to a deep learning based raft cultivation remote sensing image scene labeling method, which comprises the following four steps: step one, a computer reads data; step two, a multi-scale pure convolutional neural network is built; step three, the network is trained; step four, image labeling is performed, and a final result map is acquired. The method overcomes deficiencies in the prior art, well solves a problem of raft cultivation remote sensing image scene labeling, is high in automation degree and labeling precision, and can greatly reduce the labor cost, so that the method can be applied to labeling of raft cultivation remote sensing images and has broad application prospects and values.

Description

[0001] (1) Technical field [0002] The invention relates to a raft type breeding remote sensing image scene labeling method based on deep learning, and belongs to the technical field of visible light remote sensing image scene labeling. [0003] (2) Background technology [0004] Remote sensing is a comprehensive detection technology that uses detection instruments to record the electromagnetic wave characteristics of the target from a distance without connecting with the detection target. Through analysis, the characteristic properties and changes of the object are revealed. Remote sensing images are the product of imaging remote sensing. At this time, the electromagnetic signal of the object received by the detection instrument can be converted into an image, and vice versa, it belongs to the category of non-imaging remote sensing. [0005] Scene labeling of remote sensing images is to judge remotely sensed images pixel by pixel, marking pixels with similar features of the scene as ...

AI Technical Summary

Problems solved by technology

The labeling of raft farming remote sensing images mainly relies on manual labeling, which has a high labeling accur

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