*Artificial Intelligence Data Science Methodology for Earth Observation DOI: http://dx.doi.org/10.5772/intechopen.86886*

#### **Figure 13.**

*Advanced Analytics and Artificial Intelligence Applications*

In the case of Sentinel-1 and Sentinel-2, the images can be rectified and coaligned by publicly available toolbox routines [39]; this allowed us a straightforward

*Classification accuracy (precision/recall) for the Sentinel-2 quadrant image covering an area of the* 

While we are accustomed to image fusion as a radiometric combination of multispectral images, a comparably mature level of semantic fusion of SAR images has not been reached yet. In order to remedy the situation, we propose a semantic fusion concept for SAR images, where we combine the semantic image content of two data sets with different characteristics. By exploiting the specific imaging details and the retrievable semantic categories of the two image types, we obtained

*A multi-sensor data set: multispectral image (top-left side), panchromatic image (top-right side), and* 

*TerraSAR-X image (bottom-center) for the city of Venice, Italy.*

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**Figure 12.**

image comparison.

**Figure 11.**

*Wadden Sea.*

*Classification maps generated using the CANDELA platform for the city of Venice: multispectral image (topleft side), panchromatic image (top-right side), TerraSAR-X image (bottom-left side), and fusion of all three images (bottom-right side).*

semantically fused image classification maps that allow us to differentiate between different categories.

**Figures 12**–**14** present the classification maps for each sensor and the fused ones together with their accuracy (e.g., precision/recall) for the city of Venice, while **Figures 15**–**17** apply to the city of Munich.

For a quantitative assessment, we compared the semantic annotation results with the given reference data set and computed precision/recall for each category and sensor. Analyzing the figures separately, we observed that the average of precision/recall obtained for fused sensor images is higher than the precision/ recall of individual sensor images. Unfortunately, there are also cases in which for corresponding image patches tiled from different sensor images, the WorldView-2 annotations have a different semantic classification when compared to the TerraSAR-X results or when a category is missing for one sensor. In our case, in the Venice image, the category "buoys" is only detected in the TerraSAR-X image, and not in the WorldView-2 image. This has a noticeable impact on the performance of the category "boats." Another example is the category "clouds" that appears in the case of the Munich image that is detected in the WorldView-2 image, but not in the TerraSAR-X image.

#### **Figure 14.**

*Classification accuracy (precision/recall) for a selected image taken over the area of Venice using multispectral, panchromatic, and SAR images and also the fused image.*

#### **Figure 15.**

*A multi-sensor data set: multispectral image (top-left side), panchromatic image (top-right side), and TerraSAR-X image (bottom-center) for the city of Munich, Germany.*

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**Figure 17.**

*panchromatic, and SAR images and also the fused image.*

**Figure 16.**

*three images (bottom-right side).*

*Artificial Intelligence Data Science Methodology for Earth Observation*

*Classification maps generated using the CANDELA platform for the city of Munich: multispectral image (top-left side), panchromatic image (top-right side), TerraSAR-X image (bottom-left side), and fusion of all* 

*Classification accuracy (precision/recall) for a selected image over the area of Munich using multispectral,* 

*DOI: http://dx.doi.org/10.5772/intechopen.86886*

*Artificial Intelligence Data Science Methodology for Earth Observation DOI: http://dx.doi.org/10.5772/intechopen.86886*

#### **Figure 16.**

*Advanced Analytics and Artificial Intelligence Applications*

*Classification accuracy (precision/recall) for a selected image taken over the area of Venice using multispectral,* 

*A multi-sensor data set: multispectral image (top-left side), panchromatic image (top-right side), and* 

*TerraSAR-X image (bottom-center) for the city of Munich, Germany.*

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**Figure 15.**

**Figure 14.**

*panchromatic, and SAR images and also the fused image.*

*Classification maps generated using the CANDELA platform for the city of Munich: multispectral image (top-left side), panchromatic image (top-right side), TerraSAR-X image (bottom-left side), and fusion of all three images (bottom-right side).*

#### **Figure 17.**

*Classification accuracy (precision/recall) for a selected image over the area of Munich using multispectral, panchromatic, and SAR images and also the fused image.*
