Shadow detection is a crucial task in
high-resolution remote-sensing image processing. Various shadow detection
methods have been explored during the last decades. These methods did improve
the detection accuracy but are still not robust enough to get satisfactory results
for failing to extract enough information from the original images. To take
full advantage of various features of shadows, a new method combining edges
information with the spectral and spatial information is proposed in this
paper. As known, edge is one of the most important characteristics in the
high-resolution remote-sensing images. Unfortunately, in shadow detection, it
is a high-risk strategy to determine whether a pixel is the edge or not
strictly because intensity values on shadow boundaries are always between those
in shadow and non-shadow areas. Therefore, a soft edge description model is
developed to describe the degree of each pixel belonging to the edges or not.
Sequentially, the soft edge description is incorporating to a fuzzy clustering
procedure based on HMRF (Hidden Markov Random Fields), in which more
appropriate spatial contextual information can be used. More concretely, it
consists of two components: the soft edge description model and an iterative
shadow detection algorithm. Experiments on several remote sensing images have
shown that the proposed method can obtain more accurate shadow detection
results.
References
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