01409nam a2200229 a 450000100080000000500110000800800410001910000230006024501230008326000730020652007410027965000100102065300210103065300130105170000190106470000140108370000150109770000180111270000190113070000160114970000140116521343972021-09-14       2020    bl uuuu     u00u1 u    #d1 aPRUDENTE, V. H. R.  aSAR data for land use land cover classification in a tropical region with frequent cloud cover.h[electronic resource]  aIGARSS - INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUMc2020  aThis study aims at mapping Land Use and Land Cover (LULC) in the region of Roraima, Brazil, using time-series of Sentinel-1 Synthetic Aperture Radar (SAR) data. All available Sentinel-1 images covering the study area were used and classified using two machine learning algorithms, namely random forest and multilayer perceptron. LULC heterogeneity with the SAR process complexity makes the process challenging in distinguishing certain classes. Results show that SAR data could be used for LULC mapping, as rainforest, savannas, water, and sandbank/outcrop classes. But cannot provide accurate separation for all classes, mainly for those with similar geometrical structures, such as regeneration areas, perennial crops, and buritizais.  aRadar  aMachine learning  aSentinel1 aSANCHES, I. D.1 aADAMI, M.1 aSKAKUN, S.1 aOLDONI, L. V.1 aXAUD, H. A. M.1 aXAUD, M. R.1 aZHANG, Y.