@conference {12581,
	title = {Kernel fully constrained least squares abundance estimates},
	booktitle = {Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International},
	year = {2007},
	month = {2007/07//},
	pages = {4041 - 4044},
	abstract = {A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each end member within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.},
	keywords = {abundance, algorithm;kernel, analysis;, AVIRIS, based, constrained, constraint;feature, constraint;spectral, estimates;linear, extraction;geophysical, feature, fully, image;hyperspectral, imagery;kernel, least, mixing, model;nonnegativity, processing;geophysical, processing;multidimensional, processing;spectral, signal, space;kernel, squares, techniques;image, unmixing;sum-to-one},
	doi = {10.1109/IGARSS.2007.4423736},
	author = {Broadwater, J. and Chellapa, Rama and Banerjee, A. and Burlina, P.}
}
@conference {12691,
	title = {A hybrid algorithm for subpixel detection in hyperspectral imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2004. IGARSS {\textquoteright}04. Proceedings. 2004 IEEE International},
	volume = {3},
	year = {2004},
	month = {2004/09//},
	pages = {1601 -1604 vol.3 - 1601 -1604 vol.3},
	abstract = {Numerous subpixel detection algorithms utilizing structured backgrounds have been developed over the past few years. These range from detection schemes based on spectral unmixing to generalized likelihood ratio tests. Spectral unmixing algorithms such as the Fully Constrained Least Squares (FCLS) algorithm have the advantage of physically modeling the interactions of spectral signatures based on reflectance/emittance spectroscopy. Generalized likelihood ratio tests like the Adaptive Matched Subspace Detector (AMSD) have the advantage of identifying targets that are statistically different from the background. Therefore, a hybrid detector based on both AMSD and FCLS was developed to take advantage of each detector{\textquoteright}s strengths. Results demonstrate that the hybrid detector achieved the lowest false alarm rates while also producing meaningful abundance estimates},
	keywords = {alarm, algorithm;abundance, algorithm;Fully, algorithm;hybrid, algorithm;statistical, AMSD;Adaptive, analysis;structured, approximations;maximum, backgrounds;subpixel, constrained, detection;alarm, detection;target, Detector;FCLS, detector;hyperspectral, estimation;, estimation;emittance, identification;adaptive, imagery;reflectance, least, likelihood, Matched, matching;least, processing;geophysical, rate;generalized, ratio, signal, signatures;spectral, spectra;false, spectra;spectral, squares, subspace, systems;geophysical, techniques;image, tests;hybrid, unmixing},
	doi = {10.1109/IGARSS.2004.1370633},
	author = {Broadwater, J. and Meth, R. and Chellapa, Rama}
}
@conference {15052,
	title = {Web based progressive transmission for browsing remotely sensed imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	volume = {2},
	year = {2000},
	month = {2000///},
	pages = {591 -593 vol.2 - 591 -593 vol.2},
	abstract = {This paper describes an image representation technique that entails progressive refinement of user specified regions of interest (ROI) of large images. Progressive refinement to original quality can be accomplished in theory. However, due to heavy burden on storage resources for the authors{\textquoteright} applications, they restrict the refinement to about 25\% of the original data resolution. A wavelet decomposition of the data combined with scalar and vector quantization (VQ) of the high frequency components and JPEG/DCT compression of low frequency component is used as representation framework. Their software will reconstruct the region selected by the user from its wavelet decomposition such that it fills up the preview window with the appropriate subimages at the desired resolution, including full resolution stored for preview. Further refinement from the first preview can be obtained progressively by transmitting high frequency coefficients from low resolution to high resolution which are compressed by variant of vector quantization called model-based VQ (MVQ). The user will have an option for progressive build up of the ROIs until full resolution stored or terminate the transmission at any time during the progressive refinement},
	keywords = {based, decomposition;geophysical, image;model-based, interest;remote, interest;vector, mapping;user, mapping;vector, measurement, of, processing;geophysical, processing;image, Progressive, quantisation;wavelet, quantization;wavelet, refinement;region, regions, representation;land, representation;remote, sensing;scalar;terrain, sensing;terrain, signal, specified, specified;user, surface;large, technique;image, techniques;image, transforms;, transmission;browsing;geophysical, VQ;progressive, Web},
	doi = {10.1109/IGARSS.2000.861640},
	author = {Mareboyana,M. and Srivastava,S. and JaJa, Joseph F.}
}
@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.}
}
