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Self-adapting characteristic extracting method for optical texture images and synthetic aperture radar (SAR) images

A texture image and feature extraction technology, applied in the field of image processing, can solve the problems of insufficient fixation and unsatisfactory effect, and achieve the effect of improving the correct rate and improving the accuracy.

Inactive Publication Date: 2012-09-12
WUHAN UNIV
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Problems solved by technology

[0004] However, the general LBP feature and its related extended feature sampling are not fixed enough, and the effect is not ideal when used for the classification of optical texture images and SAR images (remote sensing images obtained by synthetic aperture radar)

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  • Self-adapting characteristic extracting method for optical texture images and synthetic aperture radar (SAR) images
  • Self-adapting characteristic extracting method for optical texture images and synthetic aperture radar (SAR) images
  • Self-adapting characteristic extracting method for optical texture images and synthetic aperture radar (SAR) images

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Embodiment Construction

[0025] Texture images have the characteristics of multiplicative non-Gaussian noise, extremely high signal-to-noise ratio, and possible random texture arrangement. The adaptive filtering provided by the present invention utilizes prior knowledge of key information of training images, and has a further guiding effect on subsequent sampling. By learning multiple images, the sampling position is random, and the learning and sampling are continuously strengthened, so as to achieve the purpose of improving the accuracy of key information sampling. Adaptive texture features have a better effect on the classification of optical texture images and SAR images.

[0026] The adaptive filtering method is the same as the LBP operator in that it also takes several key points in the neighborhood of a certain pixel point, uses the value of the pixel point as the threshold to find the label of the pixel, and then uses the label to the power of 2 as The weighted sum of the weights is used as the...

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Abstract

The invention relates to a self-adapting characteristic extracting method for optical texture images and synthetic aperture radar (SAR) images, which comprises the following steps of: learning sampling positions for a plurality of images in a training set so as to continuously learn out sampling distribution; sampling and coding image blocks for the learned sampling distribution by utilizing self-adapting filtering, and extracting self-adapting characteristics; and combining the self-adapting characteristics and original local binary pattern (LBP) characteristics in series, i.e. describing the self-adapting texture characteristics of the images. According to the self-adapting characteristic extracting method for the optical texture images and the SAR images, the distribution characteristic and the spatial characteristic of images are fused, and the prior knowledge of the images is utilized for learning. Therefore, the defect of sampling fixity of common LBP characteristics is overcome by the randomness of self-adapting sampling, and the classifying correctness of optical texture images and SAR images is enhanced so as to enhance the accuracy of the image processing application of classifying, dividing and the like based on texture characteristics.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for extracting adaptive texture feature descriptors for optical texture images and SAR images. Background technique [0002] Texture analysis is one of the main contents of texture research, and it is also an important research field in computer vision, with a very broad application background. Texture features characterize the regular changes or repetitions of image grayscale or color internal space, and as descriptors of scene structures and objects, they play an important role in image recognition—classification of different textures, etc. The application fields of texture analysis include Remotely-sensed Image Analysis, Medical Image Analysis, Industrial Surface Inspection, Document Processing and Image Retrieval. [0003] LBP (Local Binary Pattern) descriptor is a powerful means of texture description, which has grayscale invariance and rotation invariance...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 何楚许连玉廖紫纤石博
Owner WUHAN UNIV
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