Hyperspectral imagery

Buffet, D. and Oger, R. (2003) Characterisation of grassland canopy using CASI-SASI hyperspectral imagery, Presented at the CASI-SWIR2002 Workshop, 4 September, Bruges. 

Kim, M. S., Lefcourt, A. M., Chao, K., Chen, Y. R., Kim, I. and Chan, D. E. (2002) Mul-tispectral detection of fecal contamination on apples based on hyperspectral imagery Part I. Application of visible and near-infrared reflectance imaging. Trans. ASAE 45(6), 2027-37. 

DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES... 

The method presented here is based off previous research into the use of physics-based target signatures in a scheme where detection is performed in the native image radiance space. Healey Slater (1999) and Thai Healy (2002) first presented the method as a way to overcome deficiencies in atmospheric compensation of visible / near infrared / shortwave infrared (Vis / NIR / SWIR) hyperspectral imagery. In this case, variability in the at-sensor target signature manifestations is modeled through variability in properties... 

These methods were limited to the reflective portion of the electromagnetic spectrum, but have been extended to the LWIR by O Dotmell, et al. (2004, 2005). The variability in the target signatures was modeled with variations in the gas concentration and temperature state instead of the atmospheric contributions to the signal. This work used synthetic data over a range of gas temperatures and concentrations and considered both single-species and mixed-species plumes. The research preseuted here exteuds this work to application to real LWIR hyperspectral imagery of plumes iu a complex, iudustrial facility. 

Ifarraguerri, A. and Chang, C-1, Projection pursuit analysis of hyperspectral scenes. Conference on Algorithms for Multispectral and Hyperspectral Imagery IV, Proceedings of SPlE,vof 3372,1-59(1998)... 

Healey, G. and Slater, D., Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions, IEEE Transactions on Geoscience and Remote Sensing, 37(6), 2706-2717 (1999)... 

The detection algorithm is then derived in the feature space which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high dimensional feature space. Experimental results based on simulated toy-examples and real hyperspectral imagery shows that the kernel versions of these detectors outperform the conventional linear detectors. 

Underwood E, Ustin S, and Depietro D, Mapping nonnative plants using hyperspectral imagery, Remote Sens. Env., 86, 150, 2003. 

Monteiro, S. T., Minekawa, Y., Rosugi, Y, Akazawa, T., and Oda, R. (2007). Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery. ISPRS Journal of Photo grammetry and Remote Sensing, 62(1), 2-12. 

Bajesy P. andKooperR. (2005), Prediction Accuracy of Color Imagery from Hyperspectral Imagery, Proc. of SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, 5806, 330 1. 

Hyperspectral Imagery Imagery collected from dividing the electromagnetic spectrum into narrow bandwidths can be used to form images of terrain. 

Yao, H. et al (2008) Differentiation of toxigenic fungi using hyperspectral imagery. Sens. Instrum. Food Qual Saf,... 

Roessner S, Segl K, Heiden U, Kaufinann H (2001) Automated diffeientiatimi of urban surfaces based on airborne hyperspectral imagery. lEF.E Trans Geosci Remote Sens 39 1525-1532... 

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