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
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 an...
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Elsevier
2025-12-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000707 |
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| author | Minh Tri Le Khuong H. Tran Phuong D. Dao Hesham El-Askary Tuyen V. Ha Taejin Park |
| author_facet | Minh Tri Le Khuong H. Tran Phuong D. Dao Hesham El-Askary Tuyen V. Ha Taejin Park |
| author_sort | Minh Tri Le |
| collection | DOAJ |
| description | 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 spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations. |
| format | Article |
| id | doaj-art-412765f5656b4bb6be58d9b3b21b1f4d |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-12-01 |
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| spelling | doaj-art-412765f5656b4bb6be58d9b3b21b1f4d2025-08-20T03:32:00ZengElsevierScience of Remote Sensing2666-01722025-12-011210026410.1016/j.srs.2025.100264High spatial resolution crop type and land use land cover classification without labels: A framework using multi-temporal PlanetScope images and variational Bayesian Gaussian mixture modelMinh Tri Le0Khuong H. Tran1Phuong D. Dao2Hesham El-Askary3Tuyen V. Ha4Taejin Park5Geography and Geoinformation Science Department, George Mason University, Fairfax, VA 22030, USANASA Ames Research Center, Moffett Field, CA 94035, USA; Bay Area Environmental Research Institute, Moffett Field, CA 94035, USA; Corresponding author.NASA Ames Research Center, Moffett Field, CA 94035, USA.Department of Agricultural Biology, Colorado State University, Fort Collins, CO 80523, USA; Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA; School of Global Environmental Sustainability, Colorado State University, Fort Collins, CO 80523, USASchmid College of Science and Technology, Chapman University, One University Drive, Orange, CA, 92866, USA; Earth Systems Science and Data Solutions Lab, Chapman University, Orange, CA, 92866, USA; Department of Environmental Sciences, Faculty of Science, Alexandria University, Moharem Bek, Alexandria 21522, EgyptFaculty of Resource Management, Thai Nguyen University of Agriculture and Forestry, Thai Nguyen 250000, Viet Nam; Institute of Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, GermanyNASA Ames Research Center, Moffett Field, CA 94035, USA; Bay Area Environmental Research Institute, Moffett Field, CA 94035, USAPrevious 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 spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.http://www.sciencedirect.com/science/article/pii/S2666017225000707Crop typesLand use land coverHigh resolutionPlanetScopeVariational Bayesian Gaussian mixture modelUnsupervised |
| spellingShingle | Minh Tri Le Khuong H. Tran Phuong D. Dao Hesham El-Askary Tuyen V. Ha Taejin Park 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 Science of Remote Sensing Crop types Land use land cover High resolution PlanetScope Variational Bayesian Gaussian mixture model Unsupervised |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Crop types Land use land cover High resolution PlanetScope Variational Bayesian Gaussian mixture model Unsupervised |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000707 |
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