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Rain forest classification based on SAR texture
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DOI：10.1109/36.841988.png)
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摘要
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This paper applies the concepts of optimized texture segmentation to the classification of SAREX-92 data from the Amazon rain forest. Initially, a simple scene is classified using both SAR texture and Band 5 Landsat TM imagery, yielding forest and not-forest joint probabilities of 97.8% and 96.5%, respectively, When the same procedure is applied to a more complicated scene, including regenerating areas, the equivalent results are 93.8% and 67.3%. When predictable corrections for shadowing and the presence of a highway are introduced, the not-forest joint probability is improved to about 78%. The residual discrepancy is then a consequence of the different ways in which the SAR texture and TM intensity respond to regenerating areas in the scene.
Keyword:
rain forest classification
SAR texture
segmentation
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IF:
8.6
论文数:
2.1W
被引数:
10.7W
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