@conference {15544,
	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 technique, fast matrix-vector products, fast multipole methods, geophysical techniques, image fusion, Interpolation, iterative methods, nearest neighbor searching, optimal scattered data estimator, Remote sensing, remotely sensed data, scattered data points, sensor fusion, time efficiency},
	doi = {10.1109/AERO.2008.4526433},
	author = {Memarsadeghi,N. and Raykar,V.C. and Duraiswami, Ramani and Mount, Dave}
}
@conference {15543,
	title = {Image Registration and Fusion Studies for the Integration of Multiple Remote Sensing Data},
	booktitle = {Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on},
	volume = {5},
	year = {2006},
	month = {2006/05//},
	pages = {V - V},
	abstract = {The future of remote sensing will see the development of spacecraft formations, and with this development will come a number of complex challenges such as maintaining precise relative position and specified attitudes. At the same time, there will be increasing needs to understand planetary system processes and build accurate prediction models. One essential technology to accomplish these goals is the integration of multiple source data. For this integration, image registration and fusion represent the first steps and need to be performed with very high accuracy. In this paper, we describe studies performed in both image registration and fusion, including a modular framework that was built to describe registration algorithms, a Web-based image registration toolbox, and the comparison of several image fusion techniques using data from the EO-1/ALI and Hyperion sensors},
	keywords = {ALI, EO-1, fusion studies, geophysical signal processing, Hyperion sensors, image registration, Internet, multiple remote sensing data, multiple source data, Remote sensing, Web-based image registration toolbox},
	doi = {10.1109/ICASSP.2006.1661494},
	author = {Le Moigne,J. and Cole-Rhodes,A. and Eastman,R. and Jain,P. and Joshua,A. and Memarsadeghi,N. and Mount, Dave and Netanyahu,N. and Morisette,J. and Uko-Ozoro,E.}
}
@article {15594,
	title = {A Fast Implementation of the ISOCLUS Algorithm},
	journal = {IEEE 2003 International Geoscience and Remote Sensing Symposium},
	year = {2003},
	month = {2003///},
	abstract = {Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.},
	keywords = {algorithms, Cluster Analysis, Data structures, HEURISTIC METHODS, IMAGE PROCESSING, INTEGERS, LANDSAT SATELLITES, MEAN SQUARE VALUES, Remote sensing},
	url = {http://ntrs.nasa.gov/search.jsp?R=20030053175},
	author = {Memarsadeghi,Nargess and Mount, Dave and Netanyahu,Nathan S. and LeMoigne,Jacqueline}
}
@conference {15591,
	title = {Geo-registration of Landsat data by robust matching of wavelet features},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	volume = {4},
	year = {2000},
	month = {2000///},
	pages = {1610 -1612 vol.4 - 1610 -1612 vol.4},
	abstract = {The goal of our project is to build an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. Integrated within an automated mass processing/analysis system for Landsat data (REALM), the input to our registration method consists of scenes that have been geometrically and radiometrically corrected, as well as pre-processed for the detection of clouds and cloud shadows. Such pre-processed scenes are then geo-registered relative to a database of Landsat chips. This paper describes our registration process, including the use of a database of landmark chips, a feature extraction performed by an overcomplete wavelet representation, and feature matching using statistically robust techniques. Knowing the approximate longitudes and latitudes of the four corners of the scene, a subset of chips which represent landmarks included in the scene are extracted from the database. For each of these selected landmark chips, a corresponding window is extracted from the scene, and each chip-window pair is registered using our robust wavelet feature matching. First results and future directions are presented in the paper},
	keywords = {atmospheric techniques, automated mass processing/analysis system, chip-window pair, cloud shadows, Clouds, Feature extraction, feature matching, geo-registration, geometrically corrected scene, geophysical signal processing, Image matching, image registration, landmark chips, Landsat chips, Landsat data, Landsat-5 data, Landsat-7 data, overcomplete wavelet representation, pre-processed scenes, radiometrically corrected scene, REALM, Remote sensing, robust matching, robust wavelet feature matching, scenes, statistically robust techniques, sub-pixel accuracy registration, wavelet features, Wavelet transforms, window},
	doi = {10.1109/IGARSS.2000.857287},
	author = {Le Moigne,J. and Netanyahu,N. S and Masek,J. G and Mount, Dave and Goward,S. and Honzak,M.}
}
@conference {17080,
	title = {Developing the next generation of Earth science data systems: the Global Land Cover Facility},
	booktitle = {Geoscience and Remote Sensing Symposium, 1999. IGARSS {\textquoteright}99 Proceedings. IEEE 1999 International},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {616-618 vol.1 - 616-618 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A recent initiative by NASA has resulted in the formation of a federation of Earth science data partners. These Earth Science Information Partners (ESIPs) have been tasked with creating novel Earth science data products and services as well as distributing new and existing data sets to the Earth science community and the general public. The University of Maryland established its ESIP activities with the creation of the Global Land Cover Facility (GLCF). This joint effort of the Institute for Advanced Computer Studies (UMIACS) and the Department of Geography has developed an operational data archiving and distribution system aimed at advancing current land cover research efforts. The success of the GLCF is tied closely to assessing user needs as well. As the timely delivery of data products to the research community. This paper discusses the development and implementation of a web-based interface that allows users to query the authors{\textquoteright} data holdings and perform user requested processing tasks on demand. The GLCF takes advantage of a scaleable, high performance computing architecture for the manipulation of very large remote sensing data sets and the rapid spatial indexing of multiple format data types. The user interface has been developed with the cooperation of the Human-Computer Interaction Laboratory (HCIL) and demonstrates advances in spatial and temporal querying tools as well as the ability to overlay multiple raster and vector data sets. Their work provides one perspective concerning how critical earth science data may be handled in the near future by a coalition of distributed data centers},
	keywords = {Computer architecture, data archiving, data distribution system, Data systems, Distributed computing, Earth science data products, Earth science data system, ESIP, geographic information system, geographic information systems, Geography, geophysical measurement technique, geophysical signal processing, geophysical techniques, Geoscience, GIS, GLCF, Global Land Cover Facility, High performance computing, Indexing, information service, Information services, Institute for Advanced Computer Studies, land cover, NASA, next generation, PACS, Remote sensing, terrain mapping, UMIACS, University of Maryland, User interfaces, web-based interface},
	isbn = {0-7803-5207-6},
	doi = {10.1109/IGARSS.1999.773583},
	author = {Lindsay,F.E. and Townshend,J.R.G. and JaJa, Joseph F. and Humphries,J. and Plaisant, Catherine and Shneiderman, Ben}
}
