PoLSAR image classification method based on FCN and sparse-low rank subspace expression integration

Abstract

The invention discloses a PoLSAR image classification method based on FCN and sparse-low rank subspace expression integration; the method fully utilizes the powerful ability of the FCN which can automatically learn non-linear deep multiple dimensional space characteristics from PoLsar data, can utilize the advantages that a linear dimension reduction algorithm based on sparse-low rank map embedment can simultaneously capture local and global structure information of the PoLsar data, and can effectively integrate the FCN and sparse-low rank subspace expression, thus solving the PoLSAR image non-linear deep space feature extraction problem, the dimension reduction problem, the valid integration problem between polarized information and space information, and effectively solving the PoLSAR image classification problem. The method can obtain integrated multi-layer subspace characteristic information containing various types; the information contains linear and non-linear types, shallow anddeep types, local and global types, and polarized and multiple dimensioned space types, thus providing strong discrimination ability, and greatly improving PoLSAR image classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a PolSAR image classification method represented by a fully convolutional neural network and a sparse-low rank subspace. Background technique [0002] Synthetic Aperture Radar (SAR), with its almost all-day and all-weather imaging capability, plays an important role in various ground observation applications such as agriculture and forestry management, urban planning, disaster monitoring and environmental protection. Fully polarimetric SAR (Polarimetric SAR, PolSAR) is an advanced SAR system that can measure the complex scattering matrix of a medium. The scattering matrix integrates the amplitude, phase and polarization characteristics of the target, so it can greatly improve the ability to distinguish different ground objects when used in image classification tasks. PolSAR image classification is an important part of remote sensing image interpretation, and...

AI Technical Summary

Problems solved by technology

To sum up, there are four main challenges: (1) the lack of high-quality, well-labeled remote sensing data; (2) the complexity of remote sensing data hinders the learning of robust discriminative features; (3) the How to migrate between data; (4) How to choose the appropriate model depth in the case of limited computing time and training data

Therefore, it may not be realistic to rely entirely on deep learning to solve PolSAR image classification problems at present

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