A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features
This work addresses land cover classification for environmental monitoring, offering a method that generalizes well across ecoregions, though it is incremental as it builds on existing deep learning techniques.
The paper tackled land cover mapping using Sentinel-1 SAR data by combining a Swin-Unet transformer architecture with seasonal spatio-temporal features, achieving high overall accuracy across diverse regions like Amazonia, Africa, and Siberia, with notable improvements in areas with data gaps.
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.