Semi-Supervised Classification of Terrain Features in Polarimetric SAR Images using H/A/(α)over-bar and the General Four-Component Scattering Power Decompositions

被引：0

作者：

Dauphin, Stephen ^{[1
,2
]}

West, R. Derek ^{[2
]}

Riley, Robert ^{[2
]}

Simonson, Katherine M. ^{[2
]}

机构：

[1] Colorado State Univ, Dept Math, Ft Collins, CO 80523 USA

[2] Sandia Natl Labs, Albuquerque, NM 87123 USA

来源：

CONFERENCE RECORD OF THE 2014 FORTY-EIGHTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS | 2014年

关键词：

MODEL;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In an effort to enhance image classification of terrain features in fully polarimetric SAR images, this paper explores the utility of combining the results of two state-of-the-art decompositions along with a semi-supervised classification algorithm to classify each pixel in an image. Each pixel is labeled either with a pre-determined classification label, or labeled as unknown.

引用

页码：167 / 171

页数：5

共 3 条

[1] Terrain Classification of Polarimetric SAR Images Using Semi-supervised Spatial-channel Selective Kernel Network
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Wang Y.
Journal of Radars, 2021, 10 (04) : 516 - 580
[2] SEMI-SUPERVISED CLASSIFICATION OF POLARIMETRIC SAR IMAGES USING MARKOV RANDOM FIELD AND TWO-LEVEL WISHART MIXTURE MODEL
Liu, Chi
Liao, Wenzhi
Li, Heng-Chao
Wang, Rui
Philips, Wilfried
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 990 - 993
[3] Sandbank and Oyster Farm Monitoring with Multi-Temporal Polarimetric SAR Data Using Four-Component Scattering Power Decomposition
Cheng, Tzu-Yu
Yamaguchi, Yoshio
Chen, Kun-Shan
Lee, Jong-Sen
Cui, Yi
IEICE TRANSACTIONS ON COMMUNICATIONS, 2013, E96B (10) : 2573 - 2579

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