Spatial relationships-based data inconsistency detection for raster land cover

2021 ◽  
pp. 1-20
Author(s):  
Shun Kang ◽  
Shu Peng ◽  
Shanshan Qu
Keyword(s):  
Land Cover ◽  
Spatial Relationships ◽  
Data Inconsistency ◽  
Inconsistency Detection

scholarly journals TOPOLOGICAL RELATIONS-BASED DETECTION OF SPATIAL INCONSISTENCY IN GLOBELAND30

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 489-495
Author(s):  
S. Kang ◽  
J. Chen ◽  
S. Peng
Keyword(s):  
Land Cover ◽  
Data Quality ◽  
Water Body ◽  
Google Earth ◽  
Cultivated Land ◽  
Data Quality Control ◽  
Topological Relations ◽  
Land Cover Types ◽  
Inconsistency Detection ◽  
Artificial Surface

Land cover is one of the fundamental data sets on environment assessment, land management and biodiversity protection, etc. Hence, data quality control of land cover is extremely critical for geospatial analysis and decision making. Due to the similar remote-sensing reflectance for some land cover types, omission and commission errors occurred in preliminary classification could result to spatial inconsistency between land cover types. In the progress of post-classification, this error checking mainly depends on manual labour to assure data quality, by which it is time-consuming and labour intensive. So a method required for automatic detection in post-classification is still an open issue. From logical inconsistency point of view, an inconsistency detection method is designed. This method consist of a grids extended 4-intersection model (GE4IM) for topological representation in single-valued space, by which three different kinds of topological relations including disjoint, touch, contain or contained-by are described, and an algorithm of region overlay for the computation of spatial inconsistency. The rules are derived from universal law in nature between water body and wetland, cultivated land and artificial surface. Through experiment conducted in Shandong Linqu County, data inconsistency can be pointed out within 6 minutes through calculation of topological inconsistency between cultivated land and artificial surface, water body and wetland. The efficiency evaluation of the presented algorithm is demonstrated by Google Earth images. Through comparative analysis, the algorithm is proved to be promising for inconsistency detection in land cover data.

Spatial relationships between intensive land cover and residual plant species diversity in temperate farmed landscapes

2006 ◽  
Vol 43 (6) ◽  
pp. 1128-1137 ◽  
Author(s):  
SIMON M. SMART ◽  
ROBERT H. MARRS ◽  
MIKE G. LE DUC ◽  
KEN THOMPSON ◽  
ROBERT G. H. BUNCE ◽  
...  
Keyword(s):  
Species Diversity ◽  
Land Cover ◽  
Plant Species ◽  
Plant Species Diversity ◽  
Spatial Relationships ◽  
Residual Plant

scholarly journals Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5602
Author(s):  
Xudong Guan ◽  
Chong Huang ◽  
Juan Yang ◽  
Ainong Li
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Probability Distributions ◽  
Spatial Relationship ◽  
Remote Sensing Image ◽  
Spatial Relationships ◽  
Fuzzy Classifier ◽  
Land Cover Types ◽  
Remote Sensing Image Classification

Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification “smarter”. In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the “siphonic effect” produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.

Sign in / Sign up

Export Citation Format

Share Document