Remote sensing image target detection method based on cloud computing storage and deep learning

A cloud computing storage and remote sensing image technology, applied in the field of image processing, can solve the problems of low recognition efficiency and low recognition accuracy

Inactive Publication Date: 2018-03-06
LIAONING TECHNICAL UNIVERSITY
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  • Abstract
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  • Application Information

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Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: the existing rem

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  • Remote sensing image target detection method based on cloud computing storage and deep learning
  • Remote sensing image target detection method based on cloud computing storage and deep learning
  • Remote sensing image target detection method based on cloud computing storage and deep learning

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Embodiment Construction

[0087] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0088] The application principle of the present invention will be further described below in conjunction with the accompanying drawings.

[0089] The present invention provides a remote sensing image target detection method based on cloud computing storage and deep learning comprising the following steps:

[0090] S101, read the remote sensing image, and perform pseudo-color synthesis; normalize the synthesized image;

[0091] S102, store the collected remote sensing images through cloud computing batch processing, store the remote sensing images in HDFS, use the historical data to construct a training data set for the nor...

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Abstract

The invention belongs to the technical field of image processing and discloses a remote sensing image target detection method based on cloud computing storage and deep learning. The method in the invention reduces the training time through parallel deep learning, optimizes the training effect, effectively improves the image recognition efficiency and achieves the purpose of real-time recognition by using Storm real-time recognition; and is conducive to improving the recognition speed and accuracy by dividing a remote sensing image into a plurality of segmented sub-regions. Since the method inthe invention adopts a mean shift algorithm for segmentation, the algorithm is a fast and efficient cluster segmentation algorithm which can quickly and accurately obtain segmented sub-regions. In theinvention, after the mean shift algorithm is used for segmentation, a K nearest neighbor method is used for recognition. The method is the simplest among the data mining classification methods, and the operation efficiency is high.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing image target detection method based on cloud computing storage and deep learning. Background technique [0002] Remote sensing images processed by computers must be digital images. Analog images obtained by photography must be converted to analog-to-digital (A / D) by image scanners; digital data obtained by scanning must be transferred to general-purpose carriers such as CCT that can be read by general digital computers. Computer image processing should be carried out in the image processing system. The image processing system is composed of hardware (computer, monitor, digitizer, tape drive, etc.) and software (with functions of data input, output, correction, transformation, classification, etc.). Image processing mainly includes correction, transformation and classification. However, the existing remote sensing image processing has low r...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/44G06K9/62G06K9/40
CPCG06V20/13G06V10/30G06V10/34G06V10/50G06F18/214
Inventor 李建东刘万军曲海成宋艳芳冯永安
Owner LIAONING TECHNICAL UNIVERSITY
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