Single‐look classification accuracy for polarimetric SAR

Autor:	Kazuo Ouchi, Genya Saito, G. Davidson, K. Mohri, Seiho Uratsuka, N. Ishitsuka
Rok vydání:	2006
Předmět:	Synthetic aperture radar Wishart distribution Computer science business.industry Covariance matrix Maximum likelihood Polarimetry Pattern recognition Covariance Statistics General Earth and Planetary Sciences Artificial intelligence business Image resolution Classifier (UML)
Zdroj:	International Journal of Remote Sensing. 27:5073-5080
ISSN:	1366-5901 0143-1161
DOI:	10.1080/01431160500056816
Popis:	Polarimetric Synthetic Aperture Radar (SAR) systems such as ALOS‐PALSAR and Radarsat‐2 can operate in many different modes. The use of additional polarizations may require additional time and operating power and it is important to justify this by increased classification accuracy. A fully polarimetric, dual frequency AIRSAR scene from a rice‐growing area in Japan was classified by a maximum likelihood method based on the Wishart distribution. It is shown how the measured covariance matrices determine the separation accuracy between two classes. Closed form expressions are then given for the expected single‐look accuracy of the maximum likelihood classifier as a function of the class covariance matrices. This can be used to quickly compare the high spatial resolution classification performance of different polarimetric systems to decide upon a particular operating mode.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a1db07426bc242b273ad45c8883ea07a https://doi.org/10.1080/01431160500056816 Zobrazit plný text záznamu