Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947390 | Neurocomputing | 2017 | 22 Pages |
Abstract
Polarimetric satellite-borne synthetic aperture radar (PolSAR) is expected to provide land usage information globally and precisely. In this paper, we propose a unsupervised double-stage learning land state classification system using a self-organizing map (SOM) that utilizes ensemble variation vectors. We find that the Poincare sphere parameters representing the polarization state of scattered wave have specific features of the land state, in particular, in their ensemble variation rather than spatial variation. Experiments demonstrate that the proposed PolSAR double-stage SOM system generate new classes appropriately, resulting in successful fine land classification and/or appropriate new class generation.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yuto Takizawa, Fang Shang, Akira Hirose,