Flooding extent cartography with Landsat TM imagery and regularized kernel Fisher's discriminant analysis

• The flooding extent cartography is solved via remote sensing image classification.
• The regularized kernel Fisher's discriminant is introduced for flood mapping.
• Linear and non-linear uni- and multi-temporal classification settings are studied.
• The superiority of the non-linear multi-temporal approach is illustrated.

In this paper the combined use of the regularized kernel Fisher's discriminant analysis classifier (kFDA) with Landsat TM multispectral imagery is explored for flooded area cartography purposes. This classifier provides an efficient and regularized solution for the non-linear delineation of pixels corresponding to flooded surface. The flood mapping issue is tackled from both uni- and multi-temporal classification perspectives: the former recasts the problem as a classical image classification procedure – with class water as target; the latter considers the extraction of flooded area as a change detection problem – in which only the non-permanent standing water is considered as flood. As a case study is used a Landsat TM dataset of the James River in South Dakota (USA), a region that experienced a heterogeneous flooding in spring 2011. Findings from our analysis suggest that precisely delineating the exceeding water extent requires a non-linear classifier applied in a multi-temporal setting.