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

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...

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