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Unsupervised oil tank target detection method based on shape-guided significance model

A target detection, unsupervised technology, applied in the field of remote sensing image processing and scene application, can solve the problem of high computational complexity, achieve the effect of strong robustness, avoid errors, and good robustness

Active Publication Date: 2018-08-21
BEIHANG UNIV
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Problems solved by technology

Since the algorithm needs to iterate continuously and has high computational complexity, it is not suitable for large-format remote sensing images.

Method used

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  • Unsupervised oil tank target detection method based on shape-guided significance model
  • Unsupervised oil tank target detection method based on shape-guided significance model
  • Unsupervised oil tank target detection method based on shape-guided significance model

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Embodiment Construction

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0058] The embodiment of the present invention discloses an unsupervised oil tank target detection method based on a shape-guided saliency model, see the attached figure 1 , the specific steps include:

[0059] An unsupervised oil tank target detection method based on a shape-guided saliency model, comprising the following steps:

[0060] Step 1, input the remote sensing image, use the structured edge detection algorithm SED (Structured Edge Detection, SED) t...

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Abstract

The invention discloses an unsupervised oil tank target detection method based on a shape-guided significance model. The method comprises the steps of: inputting a remote-sensing image, calculating anedge response graph of the remote-sensing image, and carrying out clustering on all pixels in the remote-sensing image to form superpixels to obtain all the superpixels of the remote-sensing image; obtaining a plurality of clustering regions on the basis of all the superpixels and the edge response graph; utilizing the clustering regions to obtain round probability and a round probability graph;calculating a shape guidance-based significance graph according to all the superpixels and the round probability graph; obtaining a binary result graph through shape-guided significance graph; utilizing a binary result graph to mark oil tank regions in the remote-sensing image to obtain the target regions. Through the target detection method, oil tank targets in low-resolution remote-sensing images under different sizes and illumination conditions can be accurately detected, and the method has better robustness.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing and scene application, and more specifically relates to an unsupervised oil tank target detection method based on a shape-guided saliency model. Background technique [0002] With the rapid development of hardware technology and the wide application of remote sensing satellite technology, the detection technology of typical ground objects in remote sensing images has attracted the attention of more and more researchers. It is of vital significance, especially in the military field where a lot of remote sensing data is used, an accurate and efficient automatic target detection algorithm is urgently needed. [0003] For oil tank targets, the difficulty of oil tank target detection increases due to various factors such as the color, texture, and shape of the oil tank itself, as well as the complex external environment. However, it is precisely because of the complex external ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/13G06V10/56G06F18/28G06F18/23
Inventor 赵丹培井敏皓姜志国史振威
Owner BEIHANG UNIV
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