用哨兵1号时序数据提升土地覆盖分类精度
A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features
- 用季节性时序图像+Swin-Unet模型处理雷达数据
- 亚马逊、非洲、西伯利亚三区整体准确率高
- 适合数据不均地区，对稀疏数据鲁棒
利用卫星影像进行土地覆盖（LC）制图对环境监测至关重要。深度学习，尤其是卷积神经网络（CNN）和视觉变换器（ViTs），已显著提升分类精度。本文提出一种新方法，将基于Transformer的Swin-Unet架构与季节性合成的时空图像结合，使用哨兵1号（Sentinel-1, S1）合成孔径雷达（SAR）数据提取的时序特征进行土地覆盖类型分类，特征按季节聚类组织。研究聚焦亚马逊、非洲和西伯利亚三个区域，评估模型在不同生态区的表现。通过采用季节性特征序列而非密集时间序列，显著提升了性能，尤其在西伯利亚等存在时间数据缺口的区域（因S1数据分布不均）。结果表明，该方法在整体准确率（O.A.）上表现优异，具备良好泛化能力，即使在训练数据有限情况下也有效。
原文摘要 · Abstract (English)
Land Cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet architecture with seasonal synthesized spatio-temporal images has been employed to classify LC types using spatio-temporal features extracted from Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data, organized into seasonal clusters. The study focuses on three distinct regions - Amazonia, Africa, and Siberia - and evaluates the model performance across diverse ecoregions within these areas. By utilizing seasonal feature sequences instead of dense temporal sequences, notable performance improvements have been achieved, especially in regions with temporal data gaps like Siberia, where S1 data distribution is uneven and non-uniform. The results demonstrate the effectiveness and the generalization capabilities of the proposed methodology in achieving high overall accuracy (O.A.) values, even in regions with limited training data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。