Abstract
A scheme that utilizes satellite radiobrightness to infer surface parameters without losing the nonlinearity between the measured quantities and desired variables is examined. The approach is to incorporate products from the 1-dimensional hydrology/radiobrightness (1dH/R) model into the dynamic learning neural network (DLNN) manages the nonlinear mappings.
Original language | English |
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Pages | 1096-1098 |
Number of pages | 3 |
State | Published - 1997 |
Event | Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 3 (of 4) - Singapore, Singapore Duration: 3 Aug 1997 → 8 Aug 1997 |
Conference
Conference | Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 3 (of 4) |
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City | Singapore, Singapore |
Period | 3/08/97 → 8/08/97 |