Land cover classification of polarimetric SAR with fully convolution network and conditional random field

doi:10.11947/j.AGCS.2020.20190038

Abstract

Abstract: Aiming at the problems of low classification accuracy and poor effect in the traditional fully convolution network (FCN), and insufficient consideration on the scattering characteristics of ground object features in the traditional polarimetric synthetic aperture radar (PolSAR) land cover classification methods. To overcome this limitation, this paper proposes a land cover classification algorithm of polarimetric SAR with improved FCN and conditional random field (CRF). First of all, the Freeman and Pauli decompositions are used to model the full-polarimetric SAR image to obtain the scattering features of scattering mechanisms, and Freeman decompositions are referenced to obtain the main scattering object corresponding to the main scattering component. Learning from the FCN-Vgg19-8s network with excellent performance in the field of image classification, and considering the large amount of high-level convolution parameters and the insufficient optimization of low-level convolution model parameters, Then an improved FCN, named FCN-MD-8s, is designed though constructing multi-scale convolution group and cost function in the upper and middle layers based on FCN-Vgg19-8s to guarante dimensionality reduction and optimization of overall model parameters. Additionally, FCN-MD-8s network is trained and tested for scattering mechanisms from Freeman decomposition by Cascade-migration-learning structure. Afterwards, according to the main scattering feature corresponding to the main scattering component, the main feature object is extracted from each component prediction image to obtain a component classification result. The result of each component classification is superimposed to gain a global rough classification. Finally, the fully-connected CRF with false color image, which is visualized by Pauli coherent decomposition, is used to transfer full image information over global rough classification for fine classification. The qualitative and quantitative analyses of classification results demonstrate that the proposed algorithm has effectiveness and feasibility.

Key words: target decomposition, fully convolution network, conditional random field, multi-scale convolution group, double-cost convergence

CLC Number:

P237

ZHAO Quanhua, XIE Kailang, WANG Guanghui, LI Yu. Land cover classification of polarimetric SAR with fully convolution network and conditional random field[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(1): 65-78.

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