@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} } @article {417, title = {Operational map-guided classification of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {2940 - 2951}, abstract = {

This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual \"egg code\" maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71\% and 89\%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.

}, keywords = {Beaufort Sea, Canadian Ice Service, Fisher criterion, geophysical signal processing, gray-level cooccurrence matrix, Gulf of Saint Lawrence, image classification, image segmentation, image texture, Kolmogorov-Smirnov test, Mahalanobis distance, map-guided classification, Markov processes, Markov random field, oceanographic regions, oceanographic techniques, pixel-based ice maps, radar imaging, remote sensing by laser beam, remote sensing by radar, SAR sea ice imagery, sea ice, sea ice classification, segment distribution comparison, synthetic aperture radar}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2005.857897}, author = {P Maillard and D A. Clausi and H Deng} } @inproceedings {649, title = {Operational segmentation and classification of SAR sea ice imagery}, booktitle = {2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, An Honorary Workshop for Prof. David A. Landgrebe}, year = {2003}, abstract = {

The Canadian Ice Service (CIS) is a government agency responsible for monitoring ice-infested regions in Canada\&$\#$39;s jurisdiction. Synthetic aperture radar (SAR) is the primary tool used for monitoring such vast, inaccessible regions. Ice maps of different regions are generated each day in support of navigation operations and environmental assessments. Currently, operators digitally segment the SAR data manually using primarily tone and texture visual characteristics. Regions containing multiple ice types are identified, however, it is not feasible to produce a pixel-based segmentation due to time constraints. In this research, advanced methods for performing texture feature extraction, incorporating tonal features, and performing the segmentation are presented. Examples of the segmentation of a SAR image that is difficult to segment manually and that requires the inclusion of both tone and texture features are presented.

}, keywords = {Canada, Canadian Ice Service, environmental assessments, Feature Extraction, geophysical signal processing, government agency, ice maps, image classification, image segmentation, image texture, navigation operations, oceanographic regions, operational segmentation, pixel based segmentation, radar imaging, SAR image, SAR sea ice imagery, sea ice, synthetic aperture radar, texture visual characteristics, tone visual characteristics}, doi = {http://dx.doi.org/10.1109/WARSD.2003.1295204}, author = {D A. Clausi and H Deng} }