High spatial resolution crop type and land use land cover classification without labels: A framework using multi-temporal PlanetScope images and variational Bayesian Gaussian mixture model

作者：Minh Tri Le, Khuong H. Tran, Phuong D. Dao, Hesham El‐Askary, Tuyen V. Ha, Taejin Park · 发表于：Science of Remote Sensing · 年份：2025 · DOI：10.1016/j.srs.2025.100264 · 被引用次数：3 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Land Use and Ecosystem Services

Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3 – 5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spati...