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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Changyi Sun Neale, C.M.U. McDonnell, J.J. Heng-Da Cheng |
| Copyright Year | 1996 |
| Description | Author affiliation: Utah State Univ., Logan, UT, USA (Changyi Sun; Neale, C.M.U.) |
| Abstract | The brightness temperatures (Tbs) observed by the Special Sensor Microwave/Imager (SSM/I) radiometer are sensitive to the changes in land surface snow conditions. Previously developed SSM/I snow classification algorithms have limitations and do not work properly for terrain where forests overlay snow cover. In this study, the authors applied unsupervised cluster analysis to define 6 snow classes in Tb observations, assessing both sparseand medium-vegetated region classes. Typical SSM/I Tb signature, in terms of cluster means, of each snow class was determined by calculating the mean Tbs of the corresponding cluster. A single-hidden-layer backpropagation (backprop) artificial neural network (ANN) classifier was designed to learn the 6 Tb patterns. Classification performance, in terms of error rate (%), was as small as 2.4%. This study confirms the potential of using cluster means in ANN supervised learning, and suggests a nonlinear retrieval method towards making the inferences of snow classes from SSM/I data over varied terrain operational. Improvement is expected by identifying more SSM/I Tb signatures of different land surface types to train the ANN classifier. |
| Starting Page | 133 |
| Ending Page | 135 |
| File Size | 308544 |
| Page Count | 3 |
| File Format | |
| ISBN | 0780330684 |
| DOI | 10.1109/IGARSS.1996.516268 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1996-05-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Snow Artificial neural networks Land surface Brightness temperature Image sensors Temperature sensors Microwave sensors Microwave radiometry Classification algorithms Clustering algorithms |
| Content Type | Text |
| Resource Type | Article |
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