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Deep learning based raft cultivation remote sensing image scene labeling method

A remote sensing image and deep learning technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as slow labeling speed, achieve the effects of reducing labor costs, high degree of automation, and improving labeling efficiency

Active Publication Date: 2017-04-19
BEIHANG UNIV
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Problems solved by technology

The labeling of raft farming remote sensing images mainly relies on manual labeling, which has a high labeling accuracy, but the labeling speed is very slow. It takes about 3-4 days to complete the labeling of a remote sensing image

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  • Deep learning based raft cultivation remote sensing image scene labeling method
  • Deep learning based raft cultivation remote sensing image scene labeling method
  • Deep learning based raft cultivation remote sensing image scene labeling method

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Embodiment Construction

[0027] In order to better understand the technical solutions of the present invention, the following further describes the embodiments of the present invention in conjunction with the accompanying drawings:

[0028] The present invention is implemented under Ubuntu 14.0.4 and Caffe deep learning framework. Under the Caffe framework, first complete the network construction and configure the relevant parameters; then use the training data for training to obtain the optimized network parameters; finally use the trained network to annotate the image and stitch the annotation results.

[0029] The structure diagram of the multi-scale pure convolutional neural network based on the present invention is as follows figure 1 As shown, each box represents a layer in the neural network. The convolutional layer performs convolution operations on the input data and contains multiple sub-convolutional layers. The downsampling layer maximizes the input data according to the specific type. Or avera...

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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 ...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V20/182G06V20/176
Inventor 史振威石天阳初佳兰赵建华宋德瑞高宁
Owner BEIHANG UNIV
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