Feature-Level Fusion of Landsat 8 Data and SAR Texture Images for Urban Land Cover Classification

Each of the urban land cover types has unique thermal pattern. Therefore, thermal remote sensing can be used over urban areas for indicating temperature differences and comparing the relationships between urban surface temperatures and land cover types. On the other hand, synthetic-aperture radar (SAR) sensors are playing an increasingly important role in land cover classification due to their ability to operate day and night through cloud cover, and capturing the structure and dielectric properties of the earth surface materials. In this research, a feature-level fusion of SAR image and all bands (optical and thermal) of Landsat 8 data is proposed in order to modify the accuracy of urban land cover classification. In the proposed object-based image analysis algorithm, segmented regions of both Landsat 8 and SAR images are utilized for performing knowledge-based classification based on the land surface temperatures, spectral relationships between thermal and optical bands, and SAR texture features measured in the gray-level co-occurrence matrix space. The evaluated results showed the improvements of about 2.48 and 0.06 for overall accuracy and kappa after performing feature-level fusion on Landsat 8 and SAR data.