Land use/cover classification through multiresolution segmentation and object oriented neural networks classification

Jorge Rocha; José A. Tenedório; Sara Encarnação; Paulo Morgado

doi:10.1117/12.690762

9 October 2006 Land use/cover classification through multiresolution segmentation and object oriented neural networks classification

Jorge Rocha, José A. Tenedório, Sara Encarnação, Paulo Morgado

Author Affiliations +

Proceedings Volume 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI; 63660A (2006) https://doi.org/10.1117/12.690762
Event: SPIE Remote Sensing, 2006, Stockholm, Sweden

Abstract

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.

Citation Download Citation

Jorge Rocha, José A. Tenedório, Sara Encarnação, and Paulo Morgado "Land use/cover classification through multiresolution segmentation and object oriented neural networks classification", Proc. SPIE 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, 63660A (9 October 2006); https://doi.org/10.1117/12.690762

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