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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sanghoon Lee Crawford, M.M. |
| Copyright Year | 2000 |
| Description | Author affiliation: Dept. of Ind. Eng., Kyungwon Univ., South Korea (Sanghoon Lee) |
| Abstract | Various satellite-based sensors currently provide different information about the Earth's surface. Recently, there has been increasing interest in the use of multi-sensor data for remote sensing applications. The purpose of this study is to classify the land-cover using remotely-sensed data from multiple sources. Most of statistical classifier requires the knowledge of the number of classes and the class parameters, which are not previously known in practice. The process of collecting the information necessary for the classification, however, is very expensive. This study has developed to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. A hierarchical clustering procedure and local fuzzy classification have been employed to find the sample classes that well represent the ground truth. The maximum likelihood classifier has then used the sample classes. The combination of image bands associated with multiple channels/sensors has been selected based on the signal to noise ratio, which is the ratio of difference in class-signal and signal-dependent noise. |
| Starting Page | 957 |
| Ending Page | 959 |
| File Size | 295275 |
| Page Count | 3 |
| File Format | |
| ISBN | 0780363590 |
| DOI | 10.1109/IGARSS.2000.857988 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-07-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image classification Maximum likelihood estimation Layout Image segmentation Remote sensing Image sensors Signal to noise ratio Sensor phenomena and characterization Clustering algorithms Partitioning algorithms |
| Content Type | Text |
| Resource Type | Article |
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