@article {271, title = {Fusing AMSR-E and QuikSCAT imagery for improved sea ice recognition}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {47}, year = {2010}, pages = {1980 - 1989}, abstract = {

The benefits of augmenting Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) image data with Quick Scatterometer (QuikSCAT) image data for supervised sea ice classification in the Western Arctic region are investigated. Experiments compared the performance of a maximum likelihood classifier when used with the AMSR-E-only data set against using the combined data. The preferred number of bands to use for classification was examined, as well as whether principal component analysis (PCA) can be used to reduce the dimensionality of the data. The reliability of training data over time was also investigated. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreases it significantly when a sufficient number of bands are used. Combining these data sets is beneficial for sea ice mapping. Using all available bands is recommended, data fusion with PCA does not offer any benefit for these data, and training data from a specific date remains reliable within 30 days.

}, keywords = {Advanced Microwave Scanning Radiometer for the Earth Observing System, AMSR-E-QuikSCAT imagery fusion, data dimensionality reduction, geophysics computing, image classification, image fusion, maximum likelihood classifier, oceanographic regions, PCA, principal component analysis, Quick Scatterometer, Remote Sensing, sea ice, sea ice mapping, sea ice recognition, supervised sea ice classification, training data reliability, Western Arctic region}, doi = {http://dx.doi.org/10.1109/TGRS.2009.2013632}, author = {P Yu and D A. Clausi and S Howell} } @inproceedings {528, title = {Design and establishment of multi-scale spatial information system based on regional planning and decision making}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Fast development of Geomatics and network integrated technology demonstrates that spatial information are widely used in regional planning and decision making. Existing non-standardized multi-sources and multi-scales data have a shortage of spatial information sharing in both internal and external application departments or units, especially in the network era. A pilot regional planning and decision making spatial information system based on a standardized and network sharing technology is presented in this paper. The research area is composed of eight counties in Jinghong Region and Simao Region of Yunnan Province in China. Spatial information resources such as remote sensing, digital maps, and paper maps are collected from different departments. Statistics data such as agriculture, forest, earthquake hazard, ore, hydrogeology, ecology, transportation, population, tourism, and trade, etc. are the composition of the application system. The system will be mainly used by regional planners and managers in the province and the regions via Internet. The applications of general users are also considered. Geographic Metadata standardization for spatial information is used as an Internet engine between users and datasets. Arc/INFO and ArcView GIS are the basic software for information integration. WebGIS is used for spatial information sharing design and GeoMedia is accepted as a basic web software platform. Other software for multi-dimensional information display and management are designed and applied for virtual reality planning and application in the project

}, keywords = {agriculture, China, decision making, decision support systems, forest, geographic information system, geographic information systems, geomatics, geophysical measurement technique, geophysical signal processing, geophysical techniques, geophysics computing, GIS, Jinghong, land surface, land use, metadata, multi-dimensional information display, multiscale spatial information system, population, regional planning, Remote Sensing, sharing technology, Simao, tourism, town and country planning, vegetation, WebGIS, Yunnan Province}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977131}, author = {Y Zhao and D A. Clausi} } @inproceedings {461, title = {Multiresolution stochastic processing of Topex/Poseidon oceanographic altimetry}, booktitle = {IEEE OCEANS{\textquoteright}94}, year = {1994}, abstract = {

Remarkable advances in remote sensing are producing measurements on increasingly finer grids, offering the possibility of a far better understanding and characterization of the ocean surface. The sheer quantity of measurements has rendered traditional data smoothing/assimilation algorithms obsolete however, even on high performance workstations; the basis of this paper is the application of an extremely fast multiresolution smoothing framework to ocean altimetry data. Using the previously developed multiresolution framework, a simple multiscale model is developed which captures the characteristic power-law spectrum of the ocean surface. Smoothing altimetric data and computing error variances on a grid of 512 by 512 points is accomplished in about 10 seconds on a SUN Sparc-10. Finally, further applications of this framework within the oceanographic context are discussed, including model identification and anomaly detection

}, keywords = {algorithm, data assimilation, data smoothing, geophysical signal processing, geophysics computing, measurement technique, multiresolution stochastic processing, multiscale model, ocean dynamics, ocean surface, oceanographic techniques, Poseidon, radar altimetry, radar applications, radar imaging, radar signal processing, Remote Sensing, remote sensing by radar, satellite remote sensing, sea surface, signal processsing, Topex}, doi = {http://dx.doi.org/10.1109/OCEANS.1994.363913}, author = {P Fieguth and W C. Karl and A S. Willsky} }