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Rain forest classification based on SAR texture

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1 Author(s)
Oliver, C.J. ; DERA, Malvern, UK

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

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:38 ,  Issue: 2 )