1996
DOI: 10.1109/36.481896
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Knowledge-based land-cover classification using ERS-1/JERS-1 SAR composites

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Cited by 143 publications
(52 citation statements)
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References 23 publications
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“…In the approach by Dobson et al [29] the classifier produces two levels of classification, first a terrain differentiation into man-made features (urban), surfaces, short vegetation, and tall vegetation, followed by a level 2 differentiation of the tall vegetation class based on foliage and growth form of woody stems (excurrent, decurrent, and columnar tree architecture), leading to overall accuracies over 90% in northern Michigan. The knowledge-based SAR-based classification by Dobson et al [29] was superior to unsupervised classification of multi-temporal AVHRR data. A dictionary-and rule-based model selection approach was developed in an adaptive contextual semi-supervised algorithm for multi-temporal RADARSAT-2 polarimetric SAR (PolSAR) data [30].…”
Section: Introductionmentioning
confidence: 99%
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“…In the approach by Dobson et al [29] the classifier produces two levels of classification, first a terrain differentiation into man-made features (urban), surfaces, short vegetation, and tall vegetation, followed by a level 2 differentiation of the tall vegetation class based on foliage and growth form of woody stems (excurrent, decurrent, and columnar tree architecture), leading to overall accuracies over 90% in northern Michigan. The knowledge-based SAR-based classification by Dobson et al [29] was superior to unsupervised classification of multi-temporal AVHRR data. A dictionary-and rule-based model selection approach was developed in an adaptive contextual semi-supervised algorithm for multi-temporal RADARSAT-2 polarimetric SAR (PolSAR) data [30].…”
Section: Introductionmentioning
confidence: 99%
“…Knowledge-based models have been used to determine hierarchical decision rules to differentiate land cover classes [29]. In the approach by Dobson et al [29] the classifier produces two levels of classification, first a terrain differentiation into man-made features (urban), surfaces, short vegetation, and tall vegetation, followed by a level 2 differentiation of the tall vegetation class based on foliage and growth form of woody stems (excurrent, decurrent, and columnar tree architecture), leading to overall accuracies over 90% in northern Michigan.…”
Section: Introductionmentioning
confidence: 99%
“…Synthetic aperture radar (SAR) has been used to quantify forest cover (Ranson and Sun 1994;Dobson et al 1996;Pierce et al 1998;Simard et al 2000) and forest cover change (Ranson and Sun 1997;Rignot et al 1997;Saatchi et al 1997). SAR data used in those studies are from experimental (Ranson and Sun 1997;Rignot et al 1997;Saatchi et al 1997) and commercial SAR missions (Fransson et al 2007;Santos et al 2008).…”
Section: Introductionmentioning
confidence: 99%
“…To assess the post-hurricane tidal flushing likelihood, we considered water levels and salinities, the pattern of progressive flood maps, topographic controls, and relative backscatter attenuation as an indication of marsh flooding (e.g. Dobson, Pierce, and Ulaby 1996). Direct interpretation of backscatter decrease with flood depth increase depends on X-band perceiving marsh flooding at all depths.…”
Section: Mapping Surge Extentmentioning
confidence: 99%