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Optical remote sensing image target detection method based on geometric structure double-path convolutional network

An optical remote sensing image and geometric structure technology, applied in the field of image recognition, can solve the problems of insensitive target boundary and low positioning accuracy, and achieve the effect of strengthening the response and improving the positioning accuracy.

Active Publication Date: 2019-08-30
XIDIAN UNIV
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide an optical remote sensing image target detection method based on a two-way convolutional network with a geometric structure to solve the problem that the single-stage target detection method is not sensitive to the target boundary, which leads to the problem of positioning The problem of low precision

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  • Optical remote sensing image target detection method based on geometric structure double-path convolutional network

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[0051] The invention provides an optical remote sensing image target detection method based on a geometric structure two-way convolutional network, constructing a training data set; constructing a test data set; constructing a target detection model based on a geometric structure two-way convolutional network: a geometric structure two-way The convolutional network consists of a geometric structure area convolution network based on the DoG ridge wave kernel function and a convolutional network based on a random convolution kernel. The former only operates on the geometric structure area of ​​​​the image, and the geometric structure area is obtained through the initial sketch, which is the image The area with a sudden change in brightness often implies the shape information of the target; train the target detection model; input the test data set to the target detection model; output the detection result. The invention can improve the sensitivity of the convolutional network to t...

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Abstract

The invention discloses an optical remote sensing image target detection method based on a geometric structure double-path convolutional network. The optical remote sensing image target detection method comprises the following steps: constructing a training data set T in an image block-sketch block-label mode by using a labeled optical remote sensing image data set; constructing a test data set Uin an image block-sketch block mode by using an optical remote sensing image to be detected; constructing a target detection model based on the geometric structure double-path convolutional network,wherein the target detection model based on the geometric structure double-path convolutional network comprises a regional convolutional module and a DoG ridgelet basis function convolutional module;using the training data set T to train a target detection model based on the geometric structure double-path convolutional network to obtain a trained target detection model based on the geometric structure two-way convolutional network; and inputting the test data set U into the trained target detection model based on the geometric structure two-way convolutional network to obtain a detection result of the optical remote sensing image to be detected. According to the invention, the positioning precision of the target detection model is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to an optical remote sensing image target detection method based on a geometric structure two-way convolution network. Background technique [0002] With the development of remote sensing technology, a large number of high-resolution optical remote sensing images can provide rich spatial information and context information, which also promotes the rapid development of object detection in optical remote sensing images. For optical remote sensing images, the target detection objects that researchers focus on include aircraft, vehicles, ships, roads, and bridges. Optical remote sensing image target detection plays an important role in national defense construction, urban monitoring, cargo transportation and port management, saving a lot of manpower and material resources. [0003] With the popularity of deep learning and the background of big data, the powerful r...

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G06T3/40
CPCG06T3/4007G06V20/13G06V10/267G06F18/241
Inventor 刘芳李玲玲王哲焦李成陈璞花郭雨薇马文萍张丹
Owner XIDIAN UNIV
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