A Hierarchical Texture Model for Unsupervised Segmentation of Remotely Sensed Images
In this work a novel texture model particularly suited for unsupervised image segmentation is proposed. Any texture is represented at region level by means of a finite-state hierarchical model resulting from the superposition of several Markov chains, each associated with a different spatial direction. Corresponding to such a modeling, an optimization scheme, referred to as Texture Fragmentation and Reconstruction (TFR) algorithm, has been introduced.
The TFR addresses the model estimation problem in two sequential layers: the former “fragmentation” step allows to find the terminal states of the model, while the latter “reconstruction” step is aimed at estimating the relationships among the states which provide the optimal hierarchical structure to associate with the model. The latter step is based on a probabilistic measure, i.e, the region gain, which accounts for both the region scale and the inter-region interaction.
The proposed segmentation algorithm was tested on a segmentation benchmark and applied to high resolution remote-sensing forest images as well.
KeywordsSegmentation texture model Markov chain remote sensing forest classification
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