Classification of polarimetric SAR images based on SSae and FSALS‑SVM

A FSALS-SVM and image technology, applied in the field of image processing, can solve manual research, unrealistic problems, etc., and achieve the effect of improving classification accuracy, strengthening coherence, and excellent feature learning ability

Active Publication Date: 2017-09-22
XIDIAN UNIV
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Problems solved by technology

However, it is impractical to conduct manual research on these large-scale, complex data

Method used

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  • Classification of polarimetric SAR images based on SSae and FSALS‑SVM
  • Classification of polarimetric SAR images based on SSae and FSALS‑SVM
  • Classification of polarimetric SAR images based on SSae and FSALS‑SVM

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Embodiment Construction

[0020] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0021] Step 1. Preprocess all polarimetric SAR image data to be input to obtain unlabeled training samples and labeled training samples.

[0022] (1a) Perform Lee filtering with a sliding window size of 7×7 for all polarimetric SAR image data to be input;

[0023] (1b) Any pixel n of the polarimetric SAR image after Lee filtering can be expressed as a 3×3 covariance matrix M n :

[0024]

[0025] Among them, n∈[1,2,…,N], N is the number of pixels contained in the polarimetric SAR image, and the matrix M n The uppercase letters A~I in are all real numbers, and these letters can be composed into a column vector t n =[A B C D E F G H I] T , for the column vector t represented by each pixel n n Discharge in sequence to form the entire sample set to be classified;

[0026] (1c) Simply process the sample set to be classified obtained in (1b) to facilitate classification....

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Abstract

The invention aims to provide a polarization SAR image classification method based on an SSAE and an FSALS-SVM. According to the polarization SAR image classification method, a multi-implicit-strata structure of the stacked sparse automatic encoder (SSAE) is used for obtaining depth features which have the capacity for describing original data more intrinsically and are more suitable for classification, the fast sparse approximation least square-support vector machine (FSALS-SVM) which can obtain sparse solutions is used for replacing the Softmax commonly used in traditional deep learning and being combined with the SSAE, the classification accuracy of polarization SAR images is improved, the defect that a traditional polarization SAR image classification method based on pixels is greatly affected by speckle noise is overcome to a certain degree, and therefore coherence of homogeneous areas in classification result images is ensured.

Description

technical field [0001] The present invention relates to the field of image processing. Aiming at the problem of polarimetric SAR image classification, a polarimetric SAR image classification based on stacked sparse autoencoder (SSAE) and fast sparse approximation least squares support vector machine (FSALS-SVM) is proposed The method can be used for digital image preprocessing in fields such as aerospace images, astronomical images, and military affairs. Background technique [0002] Synthetic Aperture Radar (SAR) is widely used in remote sensing and map surveying and other fields due to its all-day and all-weather working ability, high resolution, and the ability to effectively identify camouflage and penetrate cover. In the past two decades, polarimetric SAR has been proven to be able to obtain richer surface feature information than traditional single-polarization SAR. At present, many polarimetric SAR systems around the world, such as TerraSAR-X, RADARSAT-2, ALOS-PALSAR...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 焦李成刘芳刘宸荣马文萍马晶晶王爽侯彪李阳阳
Owner XIDIAN UNIV
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