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Record #129610:
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MARC recordFeature matching from SAR Arctic data using neural metworks / P.E. Silveira, M. Van Dyne, Costas Tsatsoulis.
| Title: | Feature matching from SAR Arctic data using neural metworks / P.E. Silveira, M. Van Dyne, Costas Tsatsoulis. |
| Author(s): | Silveira, P. E. Van Dyne, M. Tsatsoulis, Costas. |
| Date: | 1994. |
| Publisher: | Piscataway, NJ: Institute of Electrical and Electronic Engineers |
| In: | IGARSS'94 : International Geoscience and Remote Sensing Symposium : Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation : California Institute of Technology, Pasadena, California USA, August 8-12, 1994. (1994.), Vol.1. |
| Abstract: | Back-propagation neural network was trained to determine how closely different sea-ice features match, considering possible deformations. Input to network is contour of each feature. Different input patterns were studied, and pixel grid representation of features contour is shown to yield better results than chain code representation. Database containing ice features from different images was used as implementation tool for extracting desired data. Efficiency of using database is shown as well as effectiveness of neural network in matching features in different images over time. |
| Notes: | In: IGARSS'94 : International Geoscience and Remote Sensing Symposium : Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation : California Institute of Technology, Pasadena, California USA, August 8-12, 1994. Vol.1. / Tammy I. Stein, ed. |
| Keywords: | 551.32 -- Glaciology. 551.326 -- Floating ice. 551.326.7 -- Sea ice. 53.087.23 -- Remote sensing. E6 -- Glaciology: floating ice. (*60) -- Arctic Ocean and adjacent waters. |
| SPRI record no.: | 129610 |
LDR 02060naa#a2200000#a#4500 001 SPRI-129610 005 20240419062044.0 007 ta 008 240419s1994####njua###|#####|0||#0#eng#d 035 ## ‡aSPRI-129610 040 ## ‡aUkCU-P‡beng‡eaacr 100 1# ‡aSilveira, P. E. 245 10 ‡aFeature matching from SAR Arctic data using neural metworks /‡cP.E. Silveira, M. Van Dyne, Costas Tsatsoulis. 260 ## ‡aPiscataway, NJ :‡bInstitute of Electrical and Electronic Engineers,‡c1994. 300 ## ‡ap. 496-498 :‡bill., diags. 500 ## ‡aIn: IGARSS'94 : International Geoscience and Remote Sensing Symposium : Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation : California Institute of Technology, Pasadena, California USA, August 8-12, 1994. Vol.1. / Tammy I. Stein, ed. 520 3# ‡aBack-propagation neural network was trained to determine how closely different sea-ice features match, considering possible deformations. Input to network is contour of each feature. Different input patterns were studied, and pixel grid representation of features contour is shown to yield better results than chain code representation. Database containing ice features from different images was used as implementation tool for extracting desired data. Efficiency of using database is shown as well as effectiveness of neural network in matching features in different images over time. 650 07 ‡a551.32 -- Glaciology.‡2udc 650 07 ‡a551.326 -- Floating ice.‡2udc 650 07 ‡a551.326.7 -- Sea ice.‡2udc 650 07 ‡a53.087.23 -- Remote sensing.‡2udc 650 07 ‡aE6 -- Glaciology: floating ice.‡2local 651 #7 ‡a(*60) -- Arctic Ocean and adjacent waters.‡2udc 700 1# ‡aVan Dyne, M. 700 1# ‡aTsatsoulis, Costas. 773 0# ‡7nnam ‡aTammy I. Stein, ed. ‡tIGARSS'94 : International Geoscience and Remote Sensing Symposium : Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation : California Institute of Technology, Pasadena, California USA, August 8-12, 1994. ‡dPiscataway, NJ : Institute of Electrical and Electronic Engineers, 1994. ‡gVol.1. ‡wSPRI-129592 917 ## ‡aUnenhanced record from Muscat, imported 2019 948 3# ‡a20240419