Land-cover classification from multiple classifiers using decision fusion based on the probabilistic graphical model

2019 ◽  
Vol 40 (12) ◽  
pp. 4560-4576 ◽  
Author(s):  
Zhao Bian ◽  
Ping Tang ◽  
Jun Yan
Keyword(s):  
Land Cover ◽  
Graphical Model ◽  
Land Cover Classification ◽  
Decision Fusion ◽  
Probabilistic Graphical Model ◽  
Multiple Classifiers

scholarly journals A Cluster Graph Approach to Land Cover Classification Boosting

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 10
Author(s):  
Lloyd Hughes ◽  
Simon Streicher ◽  
Ekaterina Chuprikova ◽  
Johan Du Preez
Keyword(s):  
Land Cover ◽  
Graphical Model ◽  
Land Cover Classification ◽  
Probabilistic Graphical Model ◽  
Observation Data ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Uncertainty Estimates ◽  
Spatio Temporal ◽  
Cluster Graph

When it comes to land cover classification, the process of deriving the land classes is complex due to possible errors in algorithms, spatio-temporal heterogeneity of the Earth observation data, variation in availability and quality of reference data, or a combination of these. This article proposes a probabilistic graphical model approach, in the form of a cluster graph, to boost geospatial classifications and produce a more accurate and robust classification and uncertainty product. Cluster graphs can be characterized as a means of reasoning about geospatial data such as land cover classifications by considering the effects of spatial distribution, and inter-class dependencies in a computationally efficient manner. To assess the capabilities of our proposed cluster graph boosting approach, we apply it to the field of land cover classification. We make use of existing land cover products (GlobeLand30, CORINE Land Cover) along with data from Volunteered Geographic Information (VGI), namely OpenStreetMap (OSM), to generate a boosted land cover classification and the respective uncertainty estimates. Our approach combines qualitative and quantitative components through the application of our probabilistic graphical model and subjective expert judgments. Evaluating our approach on a test region in Garmisch-Partenkirchen, Germany, our approach was able to boost the overall land cover classification accuracy by 1.4% when compared to an independent reference land cover dataset. Our approach was shown to be robust and was able to produce a diverse, feasible and spatially consistent land cover classification in areas of incomplete and conflicting evidence. On an independent validation scene, we demonstrated that our cluster graph boosting approach was generalizable even when initialized with poor prior assumptions.

Decision Fusion of Remote-Sensing Data for Land Cover Classification

2019 ◽  
pp. 341-382
Author(s):  
Arnaud Le Bris ◽  
Nesrine Chehata ◽  
Walid Ouerghemmi ◽  
Cyril Wendl ◽  
Tristan Postadjian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Decision Fusion ◽  
Sensing Data

scholarly journals Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

2017 ◽  
Vol 9 (8) ◽  
pp. 868 ◽  
Author(s):  
Yanfei Zhong ◽  
Qiong Cao ◽  
Ji Zhao ◽  
Ailong Ma ◽  
Bei Zhao ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Differential Evolution ◽  
Land Cover Classification ◽  
Decision Fusion ◽  
Urban Land Use ◽  
Optimal Decision ◽  
Lidar Data ◽  
Land Use Land Cover ◽  
Adaptive Differential Evolution

Decision Fusion of GA Self-Organizing Neuro-Fuzzy Multilayered Classifiers for Land Cover Classification Using Textural and Spectral Features

2008 ◽  
Vol 46 (7) ◽  
pp. 2137-2152 ◽  
Author(s):  
N.E. Mitrakis ◽  
C.A. Topaloglou ◽  
T.K. Alexandridis ◽  
J.B. Theocharis ◽  
G.C. Zalidis
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Decision Fusion ◽  
Spectral Features ◽  
Neuro Fuzzy ◽  
Self Organizing

scholarly journals Integrating MODIS and Landsat Data for Land Cover Classification by Multilevel Decision Rule

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 208
Author(s):  
Xudong Guan ◽  
Chong Huang ◽  
Rui Zhang
Keyword(s):  
Land Cover ◽  
Decision Rule ◽  
Land Cover Classification ◽  
Decision Fusion ◽  
Weighted Sum ◽  
Landsat Data ◽  
Rule Based ◽  
Level Data ◽  
Rule Based Approach ◽  
Fusion Scheme

In some cloudy and rainy regions, the cloud cover is high in moderate-high resolution remote sensing images collected by satellites with a low revisit cycle, such as Landsat. This presents an obstacle for classifying land cover in cloud-covered parts of the image. A decision fusion scheme is proposed for improving land cover classification accuracy by integrating the complementary information of MODIS (Moderate-resolution Imaging Spectroradiometer) time series data with Landsat moderate-high spatial resolution data. The multilevel decision fusion method includes two processes. First, MODIS and Landsat data are pre-classified by fuzzy classifiers. Second, the pre-classified results are assembled according to their assessed performance. Thus, better pre-classified results are retained and worse pre-classified results are restrained. For the purpose of solving the resolution difference between MODIS and Landsat data, the proposed fusion scheme employs an object-oriented weight assignment method. A decision rule based on a compromise operator is applied to assemble pre-classified results. Three levels of data containing different types of information are combined, namely the MODIS pixel-level and object-level data, and the Landsat pixel-level data. The multilevel decision fusion scheme was tested on a site in northeast Thailand. The fusion results were compared with the single data source classification results, showing that the multilevel decision fusion results had a higher overall accuracy. The overall accuracy is improved by more than 5 percent. The method was also compared to the two-level combination results and a weighted sum decision rule-based approach. A comparison experiment showed that the multilevel decision fusion rule had a higher overall accuracy than the weighted sum decision rule-based approach and the low-level combination approach. A major limitation of the method is that the accuracy of some of the land covers, where areas are small, are not as improved as the overall accuracy.

Land cover classification for the monitoring of winter grain crops within the Kyiv oblast and Mykolaiv oblast (Ukraine) with the use of MODIS data

2009 ◽  
Vol 15 (5) ◽  
pp. 16-23
Author(s):  
O.I. Sakhatsky ◽  
◽  
G.M. Zholobak ◽  
A.A. Makarova ◽  
O.A. Apostolov ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Grain Crops ◽  
Modis Data

A Probabilistic-Graphical-Model Based Approach for Representing Lineages in Uncertain Data

2011 ◽  
Vol 34 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Kun YUE ◽  
Wei-Yi LIU ◽  
Yun-Lei ZHU ◽  
Wei ZHANG
Keyword(s):  
Graphical Model ◽  
Uncertain Data ◽  
Probabilistic Graphical Model ◽  
Model Based
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