A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images
- 21 August 2006
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 44 (9), 2587-2600
- https://doi.org/10.1109/tgrs.2006.875360
Abstract
This paper proposes a novel pixel-based system for the supervised classification of very high geometrical (spatial) resolution images. This system is aimed at obtaining accurate and reliable maps both by preserving the geometrical details in the images and by properly considering the spatial-context information. It is made up of two main blocks: 1) a novel feature-extraction block that, extending and developing some concepts previously presented in the literature, adaptively models the spatial context of each pixel according to a complete hierarchical multilevel representation of the scene and 2) a classifier, based on support vector machines (SVMs), capable of analyzing hyperdimensional feature spaces. The choice of adopting an SVM-based classification architecture is motivated by the potentially large number of parameters derived from the contextual feature-extraction stage. Experimental results and comparisons with a standard technique developed for the analysis of very high spatial resolution images confirm the effectiveness of the proposed systemKeywords
This publication has 18 references indexed in Scilit:
- Object-based contextual image classification built on image segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready informationISPRS Journal of Photogrammetry and Remote Sensing, 2004
- A cognitive pyramid for contextual classification of remote sensing imagesIEEE Transactions on Geoscience and Remote Sensing, 2003
- IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic regionRemote Sensing of Environment, 2003
- A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areasIEEE Transactions on Geoscience and Remote Sensing, 2003
- A multi-scale segmentation/object relationship modelling methodology for landscape analysisEcological Modelling, 2003
- A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areasIEEE Transactions on Geoscience and Remote Sensing, 2003
- Classification and feature extraction for remote sensing images from urban areas based on morphological transformationsIEEE Transactions on Geoscience and Remote Sensing, 2003
- "Selective" region growing - an approach based on object-oriented classification routinesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Image segmentation techniquesComputer Vision, Graphics, and Image Processing, 1985