Integrating visual features in polarimetric SAR image classification

doi:10.6046/gtzyyg.2020.02.12

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Abstract

In order to improve the polarimetric synthetic aperture Radar (SAR) images classification accuracy by fully extracting variety of useful information, this paper proposes integrating visual features in SAR images classification. Firstly the authors constructed the polarimetric decomposition feature vector, then extracted texture parameters with Grayscale symbiosis matrix, and finally extracted color feature parameters by pseudo-color image. Based on constructing visual vector with texture and color parameter, the authors integrated the visual vector with the polarimetric feature vector to combine the new feature vectors. Using different feature vectors for classification of full PolSAR image, the authors made a comparative study of the classification results. The results show that the combination of visual features can effectively improve the classification accuracy of fully polarimetric SAR image.

Keywords target decomposition visual features polarimetric SAR image classification feature vector

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Issue Date: 18 June 2020

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	Pengyan HUANG
	Lijing BU
	Yongliang FAN

Cite this article:

Pengyan HUANG,Lijing BU,Yongliang FAN. Integrating visual features in polarimetric SAR image classification[J]. Remote Sensing for Land & Resources, 2020, 32(2): 88-93.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.12 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/88

Tab.1 Analysis of ground objects characteristics in the study area

Fig.1 Flowchart of algorithm in this paper

Fig.2 Experimental data

Fig.3 Polarimetric parameters

Fig.4 Textural parameters

Fig.5 Color parameters

Fig.6 Classification results

Fig.7 Comparison of classification results

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