Luiz Felipe de Almeida Furtado

TL;DR: In this article, the authors applied the Random Forests algorithm to classify dual-pol SAR images and polarimetric descriptors derived from two full-polarimetric Radarsat-2 C-band images acquired during the low and high water seasons of Lago Grande de Curuai floodplain, lower Amazon, Brazil.

TL;DR: In this paper, the authors used multitemporal PALSAR L-band radar imagery combined with object-based image analysis, data mining techniques and field data to derive vegetation structure and inundation patterns and characterize major vegetation types in varzea forests of the Mamiraua Sustainable Development Reserve.

TL;DR: L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments worldwide and may be extended not only with P ALSAR-2 data but also to forthcoming missions.

TL;DR: The objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon varzea and similar wetland environments - synthetically fused optical and SAR images or multi-sensor classification of paired SAR and optical images.

TL;DR: In this article, the authors evaluated the performance of object-based image analysis (OBIA) to discriminate vegetation physiognomies using high spatial resolution images in the mountainous region of Rio de Janeiro state.