Image segmentation partitions remote sensing images into image objects before assigning them to categorical
land cover classes. Current segmentation methods require users to invest considerable time and effort in the
search for meaningful image objects. As an alternative method we propose 'fuzzy' segmentation that offers more
flexibility in dealing with remote sensing uncertainty. In the proposed method, original bands are processed
using regression techniques to output fuzzy image regions which express degrees of membership to target land
cover classes. Contextual properties of fuzzy regions can be measured to indicate potential spectral confusion.
A 'defuzzification' process is subsequently conducted to produce the categorical land cover classes. This method
was tested using data sets of both high and medium spatial resolution. The results indicate that this approach
is able to produce classification with satisfying accuracy and requires very little user interaction.
Coastal remote sensing applications are regularly confined to single image 'snapshot' approaches which do not resolve
the dynamic processes in the required temporal resolution. This paper reports the results of a project in which the
dynamics of tidal sedimentation were monitored by multi-temporal airborne remote sensing in 10 minute time steps. The
radiance data was then converted to estimates of suspended particulate matter loading by the inversion of a hydro-optical
The results demonstrate that multi-temporal coastal remote sensing can provide information about such dynamic
processes that realistically can not be obtained by field-based research methods.