@conference {13984,
	title = {Efficient Kriging via Fast Matrix-Vector Products},
	booktitle = {Aerospace Conference, 2008 IEEE},
	year = {2008},
	month = {2008/03//},
	pages = {1 - 7},
	abstract = {Interpolating scattered data points is a problem of wide ranging interest. Ordinary kriging is an optimal scattered data estimator, widely used in geosciences and remote sensing. A generalized version of this technique, called cokriging, can be used for image fusion of remotely sensed data. However, it is computationally very expensive for large data sets. We demonstrate the time efficiency and accuracy of approximating ordinary kriging through the use of fast matrix-vector products combined with iterative methods. We used methods based on the fast Multipole methods and nearest neighbor searching techniques for implementations of the fast matrix-vector products.},
	keywords = {cokriging, data, data;scattered, efficiency;geophysical, estimator;remotely, fusion;, fusion;interpolation;iterative, matrix-vector, methods;image, methods;nearest, methods;remote, multipole, neighbor, points;time, products;fast, scattered, searching;optimal, sensed, sensing;sensor, technique;fast, techniques;iterative},
	doi = {10.1109/AERO.2008.4526433},
	author = {Memarsadeghi,N. and Raykar,V.C. and Duraiswami, Ramani and Mount, Dave}
}
@conference {15600,
	title = {A new approach to image fusion based on cokriging},
	booktitle = {Information Fusion, 2005 8th International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/07//},
	pages = {8 pp. - 8 pp.},
	abstract = {We consider the image fusion problem involving remotely sensed data. We introduce cokriging as a method to perform fusion. We investigate the advantages of fusing Hyperion with ALI. This evaluation is performed by comparing the classification of the fused data with that of input images and by calculating well-chosen quantitative fusion quality metrics. We consider the invasive species forecasting system (ISFS) project as our fusion application. The fusion of ALI with Hyperion data is studied using PCA and wavelet-based fusion. We then propose utilizing a geostatistical based interpolation method called cokriging as a new approach for image fusion.},
	keywords = {ALI;, analysis;, based, cokriging;, component, data;, forecasting, fusion, fusion;, geophysical, geostatistical, Hyperion, image, Interpolation, interpolation;, invasive, ISFS, method;, metrics;, PCA;, principal, processing;, project;, QUALITY, quantitative, remote, remotely, sensed, sensing;, sensor, sensors;, signal, species, system;, techniques;, transforms;, wavelet, wavelet-based},
	doi = {10.1109/ICIF.2005.1591912},
	author = {Memarsadeghi,N. and Le Moigne,J. and Mount, Dave and Morisette,J.}
}
@conference {14956,
	title = {Efficient algorithms for estimating atmospheric parameters for surface reflectance retrieval},
	booktitle = {Parallel Processing, 1996., Proceedings of the 1996 International Conference on},
	volume = {2},
	year = {1996},
	month = {1996/08//},
	pages = {132 -141 vol.2 - 132 -141 vol.2},
	abstract = {The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects. We introduce an efficient algorithm to estimate the optical characteristics of the TM imagery and to remove the atmospheric effects from it. Our algorithm introduces a set of techniques to significantly improve the quality of the retrieved images. We pay a particular attention to the computational efficiency of the algorithm thereby allowing us to correct large TM images quite fast. We also provide a parallel implementation of our algorithm and show its portability and scalability on several parallel machines},
	keywords = {Atmospheric, correction;atmospheric, effects;atmospheric, imagery;scalability;surface, parameters;portability;remotely, reconstruction;photoreflectance;remote, reflectance, reflectance;surface, retrieval;image, sensed, sensing;},
	doi = {10.1109/ICPP.1996.537392},
	author = {Fallah-Adl,H. and JaJa, Joseph F. and Liang,Shunlin}
}
@article {14967,
	title = {Fast algorithms for removing atmospheric effects from satellite images},
	journal = {Computational Science Engineering, IEEE},
	volume = {3},
	year = {1996},
	month = {1996///summer},
	pages = {66 - 77},
	abstract = {The varied features of the earth{\textquoteright}s surface each reflect sunlight and other wavelengths of solar radiation in a highly specific way. This principle provides the foundation for the science of satellite based remote sensing. A vexing problem confronting remote sensing researchers, however, is that the reflected radiation observed from remote locations is significantly contaminated by atmospheric particles. These aerosols and molecules scatter and absorb the solar photons reflected by the surface in such a way that only part of the surface radiation can be detected by a sensor. The article discusses the removal of atmospheric effects due to scattering and absorption, ie., atmospheric correction. Atmospheric correction algorithms basically consist of two major steps. First, the optical characteristics of the atmosphere are estimated. Various quantities related to the atmospheric correction can then be computed by radiative transfer algorithms, given the atmospheric optical properties. Second, the remotely sensed imagery is corrected by inversion procedures that derive the surface reflectance. We focus on the second step, describing our work on improving the computational efficiency of the existing atmospheric correction algorithms. We discuss a known atmospheric correction algorithm and then introduce a substantially more efficient version which we have devised. We have also developed a parallel implementation of our algorithm},
	keywords = {algorithms;reflectivity;remote, Atmospheric, based, computing;image, correction;atmospheric, effects;atmospheric, efficiency;inversion, enhancement;parallel, imagery;satellite, images;solar, implementation;reflected, particles;computational, photons;surface, procedures;parallel, radiation;atmospheric, radiation;remote, reflectance;atmospheric, remote, research;remotely, sensed, sensing, sensing;, sensing;satellite, techniques;geophysics},
	isbn = {1070-9924},
	doi = {10.1109/99.503316},
	author = {Fallah-Adl,H. and JaJa, Joseph F. and Liang, S. and Townshend,J. and Kaufman,Y.J.}
}
