Complexity / 2020 / Article / Tab 1 / Research Article
An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics Table 1 Low-level features and computing methods.
Features Parameters (rule) Parameters (test) Color features (each waveband of optical satellite image and) Max Mean Min Standard deviation Standard deviation (value range) Skewness Skewness (value range) Kurtosis Kurtosis (value range) ā Height features (LiDAR height raster) Max Mean Min Standard deviation Standard deviation (value range) Skewness Skewness (value range) Kurtosis Kurtosis (value range) ā Textural features (fused waveband of optical satellite image and) Normalized symmetrical matrix of grey-level co-occurrence matrix (GLCM) Normalized symmetrical matrix of GLCM GLCM features GLCM features Height textural features (LiDAR height raster) Normalized symmetrical matrix of grey-level co-occurrence matrix (GLCM) Normalized symmetrical matrix of GLCM GLCM features GLCM features Shape features (fused waveband of optical satellite image) Segmentation fusion [51 ] Segmentation fusion [51 ] Height shape features (LiDAR height raster) Region proposal generated by aspect difference Region proposal generated by aspect difference
There are four normalized symmetrical matrixes of GLCM in north, east, south, and west directions, respectively.
GLCM features are computed based on each normalized symmetrical matrix.