Integration of multisensor airborne data for an object based spectral classification
Integration of multisensor airborne data for object based image analysis, and spectral classification of individual trees is complicated by the multi-modal operation of complimentary sensors required for intersensor calibration. Simplified and generalized representations of sensor data impacts the a...
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| Language: | English en |
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2014
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| Online Access: | http://hdl.handle.net/1828/5607 |
| Summary: | Integration of multisensor airborne data for object based image analysis, and spectral classification of individual trees is complicated by the multi-modal operation of complimentary sensors required for intersensor calibration. Simplified and generalized representations of sensor data impacts the ability to calibrate, rectify, segment, and extract scene objects represented as differing scales. This research project examines the effect and implications of using lidar to calibrate, and rectify airborne imaging spectrometer to an appropriate resolution digital surface model. Through the use of a normalized digital canopy surface model, tree objects are detected and integrated with field surveyed species data for trees of classification interest. Canopy structure is used to segment, and extract airborne imaging spectrometer data for assessment and suitability in species classification. === Graduate |
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