Image recognition method, convolution neural network model training method and device

A convolutional neural network and recognition method technology, applied in the field of image recognition, can solve problems such as inability to effectively express complex functions, limited computing power, and inability to accurately obtain distribution ranges, etc., and achieve high water body edge detection accuracy and extraction accuracy , Improve the recognition accuracy, improve the accuracy and efficiency of the effect

Inactive Publication Date: 2019-02-12
TWENTY FIRST CENTURY AEROSPACE TECH CO LTD
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Problems solved by technology

However, this shallow learning method has limited computing power and cannot effectively express complex functions. With the increase in the number and diversity of computing samples, when

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  • Image recognition method, convolution neural network model training method and device
  • Image recognition method, convolution neural network model training method and device
  • Image recognition method, convolution neural network model training method and device

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[0038] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0039] In order to improve the recognition accuracy and recognition efficiency of water bodies in remote sensing images, an embodiment of the present invention provides an image recognition method, such as Figure 1-4 As shown, the method mainly includes:

[0040] 101. Perform preprocessing on the remote sensing image to be tested, and acquire multi-feature fusion data of the remote sensing image to be tested.

[0041] like Figu...

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Abstract

The invention discloses an image recognition method, a training method and a device of a convolution neural network model. The invention relates to the technical field of water body recognition and can solve the problems of low accuracy and low efficiency of water body recognition in remote sensing images in the prior art. The image recognition method comprises the following steps: preprocessing the remote sensing image to be measured to obtain the multi-feature fusion data; The multi-feature fusion data is input into the trained convolution neural network model for recognition to obtain the water body image. The training method of convolution neural network model includes: acquiring multi-feature fusion data of training remote sensing image; Multi-feature fusion data is used to train theinitialized convolutional neural network model to obtain the training output categories. Adjusting the model parameters according to the errors of the training output category and the mark category; The model of convolution neural network is obtained by training and adjusting the parameters of convolution neural network. The invention is widely applicable to water body identification scene of remote sensing image.

Description

technical field [0001] The invention relates to the technical field of water body recognition, in particular to an image recognition method, a convolutional neural network model training method and a device. Background technique [0002] With the development of remote sensing technology, the ability to acquire massive multi-source remote sensing images has been greatly improved at this stage, which can meet the data requirements of various application fields. Therefore, remote sensing images have also been used in many fields such as geography, land science, and ecology. extensive research and applications. When remote sensing images are applied to water body monitoring, linear waters such as rivers and planar waters such as lakes in the images are important basic geographic information. The scale will change dynamically. Whether it is possible to accurately and quickly obtain information on water bodies in different periods, and then obtain dynamic information on water bo...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V20/13G06N3/045
Inventor 何建军文强齐文文管雪华陈婷丁媛
Owner TWENTY FIRST CENTURY AEROSPACE TECH CO LTD
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