DocumentCode :
2141614
Title :
Polarimetric SAR data classification method using the self-organizing map
Author :
Hosokawa, Masafumi ; Hoshi, Takashi
Author_Institution :
Disaster Manage. Implementation & Res. Group, Nat. Res. Inst. of Fire & Disaster, Tokyo, Japan
Volume :
6
fYear :
2002
fDate :
24-28 June 2002
Firstpage :
3468
Abstract :
In this paper, we introduce a supervised classification method, which differentiates polarimetric SAR data into three categories using a self-organizing map (SOM) and a counter propagation learning approach after identifying the appropriate scattering classes. This classifier produces category maps corresponding to the Kohonen layers using training data for each scattering class. The SAR data are classified by inputting both like- and cross-polarization power elements into the learned SOM. In the experiment, PI-SAR data are employed since the resolution of aerial SAR data is higher than that of SAR data obtained from space. The proposed method yields higher-accuracy classifications than do conventional methods.
Keywords :
geophysical signal processing; image classification; learning (artificial intelligence); radar imaging; radar polarimetry; remote sensing by radar; self-organising feature maps; synthetic aperture radar; terrain mapping; Kohonen layers; PI-SAR data; SOM; aerial SAR data; counter propagation learning approach; cross-polarization power elements; like-polarization power elements; polarimetric SAR data classification method; scattering classes; self-organizing map; supervised classification method; training data; Counting circuits; Data engineering; Data mining; Disaster management; Fires; Neurons; Pixel; Scattering; Training data; Vegetation mapping;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International
Print_ISBN :
0-7803-7536-X
Type :
conf
DOI :
10.1109/IGARSS.2002.1027218
Filename :
1027218
Link To Document :
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