Texture features analysis for coastline extraction in remotely sensed images

Raimondo De Laurentiis; Silvana G. Dellepiane; Giancarlo Bo

doi:10.1117/12.454164

28 January 2002 Texture features analysis for coastline extraction in remotely sensed images

Raimondo De Laurentiis, Silvana G. Dellepiane, Giancarlo Bo

Proceedings Volume 4541, Image and Signal Processing for Remote Sensing VII; (2002) https://doi.org/10.1117/12.454164
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

The accurate knowledge of the shoreline position is of fundamental importance in several applications such as cartography and ships positioning1. Moreover, the coastline could be seen as a relevant parameter for the monitoring of the coastal zone morphology, as it allows the retrieval of a much more precise digital elevation model of the entire coastal area. The study that has been carried out focuses on the development of a reliable technique for the detection of coastlines in remotely sensed images. An innovative approach which is based on the concepts of fuzzy connectivity and texture features extraction has been developed for the location of the shoreline. The system has been tested on several kind of images as SPOT, LANDSAT and the results obtained are good. Moreover, the algorithm has been tested on a sample of a SAR interferogram. The breakthrough consists in the fact that the coastline detection is seen as an important features in the framework of digital elevation model (DEM) retrieval. In particular, the coast could be seen as a boundary line all data beyond which (the ones representing the sea) are not significant. The processing for the digital elevation model could be refined, just considering the in-land data.

Citation Download Citation

Raimondo De Laurentiis, Silvana G. Dellepiane, and Giancarlo Bo "Texture features analysis for coastline extraction in remotely sensed images", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454164

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