In this paper is presented a land use/cover classification methodology of the rural/urban fringe, by means of the
application of a neuronal network, with resource to the multiresolution image segmentation, construction of complex
elements through object oriented analysis and integration of not spectral (ancillary) information. The study area is the
municipality of Almada, located in the south bank of Tagus River and corresponding to one of the core regions of the
Lisbon Metropolitan Area (Portugal). The data used was 2004 HRVIR SPOT images fused with supermode
panchromatic image and the Portuguese urban quarter statistical data. The developed procedure is based in five steps: 1)
Legend creation; 2) deriving statistical ancillary data; 3) deriving object (texture) data; 4) deriving spectral data and 5)
neural network classification.
The main purpose of the research presented in this paper is the development and validation, through the application to a case study, of an efficient form of satellite image classification that integrates ancillary information (Census data; the Municipal Mater Plan; the Road Network) and remote sensing data in a Geographic Information System. The developed procedure follows a layered classification approach, being composed by three main stages: 1) Pre- classification stratification; 2) Application of Bayesian and Maximum-likelihood classifiers; 3) Post-classification sorting. Common approaches incorporate the ancillary data before, during or after classification. In the proposes method, all the steps take the auxiliary information into account. The proposed method achieves, globally, much better classification results than the classical, one layer, Minimum Distance and Maximum-likelihood classifiers. Also, it greatly improves the accuracy of those classes where the classification process uses the ancillary data.
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