Abstract
In this paper, the 100 meter JERS-1 Amazon mosaic image was used in a new classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1km resolution mean backscatter and seven first order texture measures derived from the 100 m data by using a 10 × 10 independent sampling window. The classification approach included two independent stages: 1) a supervised maximum a posteriori Baysian approach to classify the mean backscatter image into 5 general land cover categories of forest, savanna, inundated, white sand, and anthropogenic vegetation classes, and 2) a texture measure decision rule approach to further discriminate subcategory classes based on taxonime information and biomass levels. Fourteen classes were successfully separated at 1km scale. The results were verified by examining the accuracy of the approach by comparison with the IBGE and the AVHRR 1 km resolution land cover maps.
| Original language | English (US) |
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| Pages | 934-936 |
| Number of pages | 3 |
| State | Published - Dec 1 1999 |
| Event | Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century' - Hamburg, Ger Duration: Jun 28 1999 → Jul 2 1999 |
Other
| Other | Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century' |
|---|---|
| City | Hamburg, Ger |
| Period | 6/28/99 → 7/2/99 |
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ASJC Scopus subject areas
- Computer Science Applications
- Earth and Planetary Sciences(all)