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+technology.+This+study+applies+machine+learning+to+extract+forest+height+data+from+two+SAR+products%3a+Single+Look+Complex+(SLC)+images+and+tomographic+cubes%252C+in+preparation+for+the+ESA+Biomass+Satellite+mission.+We+use+the+TomoSense+dataset%252C+containing+SAR+and+LiDAR+data+from+Germany%27s+Eifel+National+Park%252C+to+develop+and+evaluate+height+estimation+models.+Our+approach+includes+classical+methods%252C+deep+learning+with+a+3D+U-Net%252C+and+Bayesian-optimized+techniques.+By+testing+various+SAR+frequencies+and+polarimetries%252C+we+establish+a+baseline+for+future+height+and+biomass+modelling.+Best-performing+models+predict+forest+height+to+be+within+2.82m+mean+absolute+error+for+canopies+around+30m%252C+advancing+our+ability+to+measure+global+carbon+stocks+and+support+climate+action.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.6405300d16f41b8a4545b000ae3e8d1b%3frik%3dBfGncqkjz4yvsg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=SAR+Tomography&FORM=IRPRST&ck=951427AF432A1B6D04CD3A5E6AC881E0&selectedIndex=2&itb=0)
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