Abstract
Deep learning models have shown encouraging capabilities for mapping accurately forests at medium resolution with TanDEM-X interferometric SAR data. Such models, as most of current state-of-the-art deep learning techniques in remote sensing, are trained in a fully-supervised way, which requires a large amount of labeled data for training and validation. In this work, our aim is to exploit the high-resolution capabilities of the TanDEM-X mission to map forests at 6 m. The goal is to overcome the intrinsic limitations posed by midresolution products, which affect, e.g., the detection of narrow roads within vegetated areas and the precise delineation of forested regions contours. To cope with the lack of extended reliable reference datasets at such a high resolution, we investigate self-supervised learning techniques for extracting highly informative representations from the input features, followed by a supervised training step with a significantly smaller number of reliable labels. A 1 m resolution forest/non-forest reference map over Pennsylvania, USA, allows for comparing different training approaches for the development of an effective forest mapping framework with limited labeled samples. We select the best-performing approach over this test region and apply it in a real-case forest mapping scenario over the Amazon rainforest, where only very few labeled data at high resolution are available. In this challenging scenario, the proposed self-supervised framework significantly enhances the classification accuracy with respect to fully-supervised methods, trained using the same amount of labeled data, representing an extremely promising starting point for large-scale, very high-resolution forest mapping with TanDEM-X data.
Abstract (translated)
深度学习模型在利用TanDEM-X干涉SAR数据进行中分辨率森林地图绘制方面展示了令人鼓舞的能力。这类模型，以及大多数当前遥感领域的先进深度学习技术，都是通过完全监督的方式训练的，这需要大量标记的数据用于训练和验证。在这项工作中，我们的目标是利用TanDEM-X任务的高分辨率能力来在6米尺度上进行森林制图。该目标旨在克服中等分辨率产品内在限制的影响，例如，在植被区域检测狭窄道路以及精确界定森林地区轮廓的问题。 为了应对这种高分辨率下可靠参考数据集缺乏的情况，我们研究了自监督学习技术以从输入特征中提取高度相关信息，并随后使用少量可靠的标签进行监督训练。美国宾夕法尼亚州1米分辨率的森林/非森林参考图允许我们比较不同训练方法，以便开发一个有效的森林制图框架，在这种情况下标注样本数量有限。 在测试区域中选择了最佳性能的方法后，我们在亚马逊雨林的真实案例森林制图场景中应用了该方法。在这个具有挑战性的场景中，仅存在很少的高分辨率标签数据，提出的自监督框架显著提高了分类准确率，相对于完全监督的方法而言，在使用相同量级标记数据的情况下效果更佳。这为利用TanDEM-X数据进行大规模、极高分辨率森林制图提供了一个极具前景的起点。
URL
https://arxiv.org/abs/2505.03327
