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dc.contributor.authorBostater, Charles R.
dc.date.accessioned2017-05-22T16:48:05Z
dc.date.available2017-05-22T16:48:05Z
dc.date.issued2015-10-21
dc.identifier.citationBostater, C. R. (2015). An approach to optimal hyperspectral and multispectral signature and image fusion for detecting hidden targets on shorelines. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 9653 doi:10.1117/12.2196293en_US
dc.identifier.urihttp://hdl.handle.net/11141/1432
dc.description.abstractHyperspectral and multispectral imagery of shorelines collected from airborne and shipborne platforms are used following pushbroom imagery corrections using inertial motion motions units and augmented global positioning data and Kalman filtering. Corrected radiance or reflectance images are then used to optimize synthetic high spatial resolution spectral signatures resulting from an optimized data fusion process. The process demonstrated utilizes littoral zone features from imagery acquired in the Gulf of Mexico region. Shoreline imagery along the Banana River, Florida, is presented that utilizes a technique that makes use of numerically embedded targets in both higher spatial resolution multispectral images and lower spatial resolution hyperspectral imagery. The fusion process developed utilizes optimization procedures that include random selection of regions and pixels in the imagery, and minimizing the difference between the synthetic signatures and observed signatures. The optimized data fusion approach allows detection of spectral anomalies in the resolution enhanced data cubes. Spectral-spatial anomaly detection is demonstrated using numerically embedded line targets within actual imagery. The approach allows one to test spectral signature anomaly detection and to identify features and targets. The optimized data fusion techniques and software allows one to perform sensitivity analysis and optimization in the singular value decomposition model building process and the 2-D Butterworth cutoff frequency and order numerical selection process. The data fusion ynthetic imagery? forms a basis for spectral-spatial resolution enhancement for optimal band selection and remote sensing algorithm development within pectral anomaly areas?. Sensitivity analysis demonstrates the data fusion methodology is most sensitive to (a) the pixels and features used in the SVD model building process and (b) the 2-D Butterworth cutoff frequency optimized by application of K-S nonparametric test. The image fusion protocol is transferable to sensor data acquired from other platforms, including moving platforms as demonstrated.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with the publisher's policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleAn approach to optimal hyperspectral and multispectral signature and image fusion for detecting hidden targets on shorelinesen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.2196293


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