The Brazilian savanna (Cerrado) is considered a hotspot for conservation. Despite its environmental and social importance, the biome has suffered a rapid transformation process due to human activities. Mapping and monitoring the remaining vegetation is essential to guide public policies for biodiversity conservation. However, accurately mapping the Cerrado’s vegetation is still an open challenge. Its diverse but spectrally similar physiognomies are a source of confusion for state-of-the-art (SOTA) methods. This study proposes a deep learning model to map the natural vegetation of the Cerrado at the regional to biome level, fusing Synthetic Aperture Radar (SAR) and optical data. The proposed model is designed to deal with uncertainties caused by the different resolutions of the input Sentinel-1/2 images (10 m) and the reference data, derived from Landsat images (30 m). We designed a multi-resolution label-propagation (MRLP) module that infers maps at both resolutions and uses the class scores from the 30 m output as features for the 10 m classification layer. We train the model with the proposed calibrated dual focal loss function in a 2-stage hierarchical manner. Our results reached an overall accuracy of 70.37%, representing an increase of 15.64% compared to a SOTA random forest (RF) model. Moreover, we propose an uncertainty quantification method, which has shown to be useful not only in validating the model, but also in highlighting areas of label noise in the reference. The developed codes and dataset are available on Github.

中文翻译：

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绘制巴西稀树草原的自然植被图：SAR 光学不确定性感知深度学习方法


巴西稀树草原（塞拉多）被认为是保护热点。尽管生物群落具有环境和社会重要性，但由于人类活动，生物群落经历了快速转变过程。绘制和监测剩余植被对于指导生物多样性保护公共政策至关重要。然而，准确绘制塞拉多植被地图仍然是一个公开的挑战。其多样化但光谱相似的面貌是最先进（SOTA）方法的混乱根源。本研究提出了一种深度学习模型，融合合成孔径雷达 (SAR) 和光学数据，从区域到生物群落层面绘制塞拉多自然植被地图。所提出的模型旨在处理由输入 Sentinel-1/2 图像 (10 m) 和源自 Landsat 图像 (30 m) 的参考数据的不同分辨率引起的不确定性。我们设计了一个多分辨率标签传播 (MRLP) 模块，该模块可推断两种分辨率下的地图，并使用 30 m 输出的类别分数作为 10 m 分类层的特征。我们使用所提出的校准双焦点损失函数以两阶段分层方式训练模型。我们的结果总体准确率达到 70.37%，与 SOTA 随机森林 (RF) 模型相比提高了 15.64%。此外，我们提出了一种不确定性量化方法，该方法已被证明不仅在验证模型方面有用，而且在突出参考中的标签噪声区域方面也很有用。开发的代码和数据集可在 Github 上获取。