Polarized SAR image classification method based on sparse coding and wavelet auto-encoder

A sparse auto-encoder and sparse coding technology, applied in the field of polarization synthetic aperture radar SAR image classification, can solve the problems of not having time-frequency local properties, not considering the spatial correlation of polarization SAR images, and large amount of calculation, etc. Good time-frequency local properties, excellent feature expression ability, and the effect of removing coherent speckle noise

Active Publication Date: 2016-11-09
XIDIAN UNIV
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Problems solved by technology

Although this method combines the histogram segmentation of the scattering entropy H and the scattering angle α to obtain the division threshold, it still has the disadvantage that the method does not effectively combine the neighborhood information of the data and does not consider the polarimetric SAR image. Spatial correlat

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  • Polarized SAR image classification method based on sparse coding and wavelet auto-encoder
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  • Polarized SAR image classification method based on sparse coding and wavelet auto-encoder

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[0046] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0047] refer to figure 1 , to further describe in detail the specific implementation steps of the present invention.

[0048]Step 1, input image.

[0049] Input the covariance matrix of a polarimetric SAR image to be classified. The source of the polarimetric SAR data is the L-band data acquired by the NASA / JPL laboratory AIRSAR sensor in the San Francisco Bay Area in 2008. The resolution is 10*5m, and the size It is 1800*1380 pixels. The size of the covariance matrix of the image is 3*3*N, where N is the total number of pixels in the polarimetric SAR image.

[0050] Input the real object marker image of the polarization synthetic aperture radar SAR image to be classified.

[0051] Step 2, preprocessing.

[0052] The refined Lee filter is used to filter the covariance matrix to remove the speckle noise, and the filtered matrix of each pixel of the polarimetric S...

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Abstract

The invention discloses a polarized SAR (Synthetic Aperture Radar) image classification method based on sparse coding and a wavelet auto-encoder, mainly solving the problems of boundary classification caused by unreasonable characteristic extraction and poor region homogeneity caused by not considering spatial correlation. The method mainly comprises the steps of: (1) inputting images; (2) pre-processing; (3) extracting image characteristics; (4) sparse coding; (5) selecting a training sample and a test sample; (6) training a wavelet auto-encoder; (7) training a softmax classifier; (8) adjusting network parameters; (9) classifying the images; (10) coloring; and (11) outputting a classification result image. The method has a better denoising effect, considers data neighborhood information, and can better learn characteristics of a higher level from low-dimensional characteristics, allow a classification result image to have a clearer outline and edge, and improve polarized SAR image classification performance.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarization synthetic aperture radar SAR (Synthetic Aperture Radar) image classification method based on sparse coding and wavelet sparse autoencoder in the technical field of polarization synthetic aperture radar image classification. The invention adopts a method combining Gaussian pyramid pooling encoding and wavelet sparse self-encoder to classify polarimetric synthetic aperture radar SAR images, and the method can be used for polarimetric synthetic aperture radar SAR image target detection and target recognition. Background technique [0002] Polarization SAR has become one of the important development directions of SAR at home and abroad, and polarization SAR image classification is an important research technology of SAR image interpretation. Polarimetric SAR is an active high-resolution active microwave remote sensing imaging radar. Its research began in...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24
Inventor 焦李成屈嵘吴妍马文萍尚荣华马晶晶张丹侯彪杨淑媛
Owner XIDIAN UNIV
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