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SAR image ship target detection method on basis of superpixel and random forest

A technology of target detection and random forest, applied in the field of detection of high-resolution SAR images, can solve problems such as difficult to obtain data

Active Publication Date: 2018-08-14
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

But classification requires a large amount of training data, and in most cases, it is difficult to obtain enough data for training

Method used

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  • SAR image ship target detection method on basis of superpixel and random forest
  • SAR image ship target detection method on basis of superpixel and random forest
  • SAR image ship target detection method on basis of superpixel and random forest

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Embodiment Construction

[0035] The present invention will be further described below by taking two TerraSAR images intercepted from the Strait of Gibraltar in the United Kingdom with a resolution of 3 meters as examples.

[0036] Will figure 2 .(a) Divided into 1008 superpixel regions, the obtained division results are as follows figure 2 .(b). The irregular division preserves the outline information of the ship. figure 2 (d) is the detection result of the present invention, which shows that 9 ship targets are all detected correctly, and their shapes are well preserved. However, when using the general sliding window segmentation method and the method based on gray-level clustering, such as figure 1 As shown in .(c), the loss of ship information is serious. In order to see the details of the detection results, we have figure 2 3 ship targets are marked in , and the detection results are as follows image 3 shown. image 3 The second line is the result obtained by using the sliding window to...

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Abstract

The invention relates to the technical field of high-resolution SAR images, in particular to a ship target detection method on the basis of superpixel and a random forest. Superpixel segmentation andrandom forest non-supervision clustering are blended, the image is partitioned and then clustered, a rare ship target is accurately detected, and meanwhile contour shape information of the ship is better reserved. Compared with a traditional CFAR chip target detection method, time-consuming and labor-consuming operations such as clutter modeling, parameter estimation and window sliding are avoided.

Description

technical field [0001] The invention relates to the technical field of detection of high-resolution SAR images, in particular to a detection method with low damage to ship shape information: SAR image ship target detection based on superpixels and random forests. Background technique [0002] Synthetic Aperture Radar (SAR) can monitor the earth in real time around the clock. Different ground objects have different scattering characteristics, and SAR images can provide such distinguishing features for scene interpretation. High-resolution SAR images contain more structure and shape information and weak scatterers, which are difficult to detect by traditional detection methods. [0003] Vessel detection, as an important maritime application, has been extensively studied. Constant false alarm rate detection (CFAR) is the most traditional SAR image ship detection technology. Based on hypothesis testing, it obtains an adaptive threshold according to the distribution of the bac...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T7/40G06T7/46G06T7/90
CPCG06T7/40G06T7/46G06T7/90G06T2207/10044G06F18/23213
Inventor 崔宗勇谭秀兰曹宗杰闵锐皮亦鸣
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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