Comparing high spatial resolution image simplifications before a segmentation process

Florence Laporterie-Dejean; Erick Lopez-Ornelas; Guy Flouzat

doi:10.1117/12.510992

5 February 2004 Comparing high spatial resolution image simplifications before a segmentation process

Florence Laporterie-Dejean, Erick Lopez-Ornelas, Guy Flouzat

Proceedings Volume 5238, Image and Signal Processing for Remote Sensing IX; (2004) https://doi.org/10.1117/12.510992
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

The spatial resolution of remotely sensed imaging devices becomes higher and higher. Since the launch of QuickBird, during the year 2001, panchromatic images of 66 cm are acquired. As those improvements have been performed very quickly, methods for an automatic processing of those images are not yet available. The improvement of the spatial resolution enables the detection of new kind of objects. For example, instead of detecting forests, trees are. New applications, notably an accurate survey of the environment during exceptional events (flooding, fire, ...) are conceivable. However, the areas which used to be homogeneous within a 10-meter resolution, are then heterogeneous. Consequently, commonly used methods, such as classification for example, are less efficient. It is urging to propose techniques for an automatic exploitation of this kind of images. In this paper, we propose to add, before the commonly used processes a pre-processing to simplify an image by the diminution of the heterogeneity within regions corresponding to a unique entity, while keeping the borders. For so doing, we compare several filters, linear and non linear. In particular, we use a morphological pyramid based filtering. An example is shown on a QuickBird image acquired over Berne, and comparison of all the filters is done.

Citation Download Citation

Florence Laporterie-Dejean, Erick Lopez-Ornelas, and Guy Flouzat "Comparing high spatial resolution image simplifications before a segmentation process", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); https://doi.org/10.1117/12.510992

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