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Abstract

The effects of applying pre-processing and post-processing to remote sensing data are studied both in the spatial image domain and the feature domain. A neural network is used for classification since it is not biased by a priori assumptions about the distributions of the spectral values of the classes. Spatial smoothing was applied both as pre- and post-processing steps. Pre-processing involved smoothing the image spectral values by means of anisotropic diffusion, whereas iterative majority filtering is applied as a post-processing step to improve spatial coherence by reclassifying pixels. The effects of all spatial and spectral filtering methods were validated by applying them to three different test cases.

Additional information

Authors: FIERENS F, JRC Ispra (IT);ROSIN P, JRC Ispra (IT)
Bibliographic Reference: Paper presented: EUROPTO, Roma (IT), September 26-30, 1994
Availability: Available from (1) as Paper EN 38575 ORA
Record Number: 199510432 / Last updated on: 1995-04-11
Category: PUBLICATION
Original language: en
Available languages: en