Structural Damage Assessments from Ikonos Data Using Change Detection, Object-Oriented Segmentation, and Classification Techniques
Recent improvements in the spatial resolution of commercial satellite imagery make it possible to apply very high-resolution (VHR) satellite data for assessing structural damage in the aftermath of humanitarian crises, such as, armed conflicts. Visual interpretation of pre- and post-crisis
very high-resolution satellite imagery is the most straightforward method for discriminating structural damage and assessing its extent. However, the feasibility of using visual interpretation alone diminishes in the cases of large and dense urban settlements and spatial resolutions in the
range of 2 m to 3 meters and larger. Visual interpretation can be further complicated at spatial resolutions greater than 1 m if accompanied by shadow formation and differences in sensor and solar conditions between the pre- and post-conflict images.
In this study, we address these problems through investigating the use of traditional change techniques, namely, image differencing and principle component analysis, with an object-oriented image classification software, e-Cognition. Pre-conflict Ikonos (2 m resolution) images of Jenin in the Palestinian territories and Brest (1 m resolution) in FYROM were classified using the e-Cognition software. Thereafter, the pre-conflict classification was used to guide the classification, using e-Cognition, of the pixel-based change detection analysis. The second part of the study examines the feasibility of using mathematical morphological operators to automatically identify likely structurally damaged zones in dense urban settings. The overall results are promising and show that object-oriented segmentation and classification systems facilitate the interpretation of change detection results derived from very high-resolution (1 m and 2 m) commercial satellite data. The results show that object-oriented classification techniques enhance quantitative analysis of traditional pixel-based change detection applied to very high-resolution satellite data and facilitate the interpretation of changes in urban features. Finally, the results suggest that mathematical morphological methods are a potential new avenue for automatically extracting likely damaged zones from very high-resolution satellite imagery in the aftermath of disasters.
In this study, we address these problems through investigating the use of traditional change techniques, namely, image differencing and principle component analysis, with an object-oriented image classification software, e-Cognition. Pre-conflict Ikonos (2 m resolution) images of Jenin in the Palestinian territories and Brest (1 m resolution) in FYROM were classified using the e-Cognition software. Thereafter, the pre-conflict classification was used to guide the classification, using e-Cognition, of the pixel-based change detection analysis. The second part of the study examines the feasibility of using mathematical morphological operators to automatically identify likely structurally damaged zones in dense urban settings. The overall results are promising and show that object-oriented segmentation and classification systems facilitate the interpretation of change detection results derived from very high-resolution (1 m and 2 m) commercial satellite data. The results show that object-oriented classification techniques enhance quantitative analysis of traditional pixel-based change detection applied to very high-resolution satellite data and facilitate the interpretation of changes in urban features. Finally, the results suggest that mathematical morphological methods are a potential new avenue for automatically extracting likely damaged zones from very high-resolution satellite imagery in the aftermath of disasters.
Document Type: Research Article
Publication date: 01 July 2005
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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