@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 {14927,
	title = {A hierarchical data archiving and processing system to generate custom tailored products from AVHRR data},
	booktitle = {Geoscience and Remote Sensing Symposium, 1999. IGARSS {\textquoteright}99 Proceedings. IEEE 1999 International},
	volume = {5},
	year = {1999},
	month = {1999///},
	pages = {2374 -2376 vol.5 - 2374 -2376 vol.5},
	abstract = {A novel indexing scheme is described to catalogue satellite data on a pixel basis. The objective of this research is to develop an efficient methodology to archive, retrieve and process satellite data, so that data products can be generated to meet the specific needs of individual scientists. When requesting data, users can specify the spatial and temporal resolution, geographic projection, choice of atmospheric correction, and the data selection methodology. The data processing is done in two stages. Satellite data is calibrated, navigated and quality flags are appended in the initial processing. This processed data is then indexed and stored. Secondary processing such as atmospheric correction and projection are done after a user requests the data to create custom made products. By dividing the processing in to two stages saves time, since the basic processing tasks such as navigation and calibration which are common to all requests are not repeated when different users request satellite data. The indexing scheme described can be extended to allow fusion of data sets from different sensors},
	keywords = {archiving;image, AVHRR;GIS;PACS;custom, data, image;land, image;remote, mapping;, mapping;PACS;geophysical, measurement, PROCESSING, processing;geophysical, product;data, remote, scheme;infrared, sensing;optical, sensing;terrain, signal, surface;multispectral, system;indexing, tailored, technique;hierarchical, techniques;remote},
	doi = {10.1109/IGARSS.1999.771514},
	author = {Kalluri, SNV and Zhang,Z. and JaJa, Joseph F. and Bader, D.A. and Song,H. and El Saleous,N. and Vermote,E. and Townshend,J.R.G.}
}
@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.}
}
@conference {14955,
	title = {Efficient Algorithms for Atmospheric Correction of Remotely Sensed Data},
	booktitle = {Supercomputing, 1995. Proceedings of the IEEE/ACM SC95 Conference},
	year = {1995},
	month = {1995///},
	pages = {12 - 12},
	abstract = {Remotely sensed imagery has been used for developing and validating various studies regarding land cover dynamics. However, the large amounts of imagery collected by the satellites are largely contaminated by the effects of atmospheric particles. The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects. We introduce a number of computational techniques that lead to a substantial speedup of an atmospheric correction algorithm based on using look-up tables. Excluding I/O time, the previous known implementation processes one pixel at a time and requires about 2.63 seconds per pixel on a SPARC-10 machine, while our implementation is based on processing the whole image and takes about 4-20 microseconds per pixel on the same machine. We also develop a parallel version of our algorithm that is scalable in terms of both computation and I/O. Experimental results obtained show that a Thematic Mapper (TM) image (36 MB per band, 5 bands need to be corrected) can be handled in less than 4.3 minutes on a 32-node CM-5 machine, including I/O time.},
	keywords = {Atmospheric, AVHRR;, computing;, correction;, high, I/O;, Parallel, performance, processing;, remote, scalable, sensing;, TM;},
	doi = {10.1109/SUPERC.1995.242453},
	author = {Fallah-Adl,H. and JaJa, Joseph F. and Liang,Shunlin and Kaufman,Y.J. and Townshend,J.}
}
