## 3 Data and Methodology
### Research Methods
#### 3.2.3. OBIA-CNN Integration Frameworks  
The Theory of OBIA-CNN Integration  
The main problem arises from the integration between OBIA-CNN due to the incompatibility between CNN input and OBIA segments, in general, the rectangular shape of CNN patch will never be compatible with the non-homogeneous shape of the segment (S) that resulted from OBIA method. For example, when performing OBIA classification based on patch-based CNN, the non-homogeneous segment is clipped based on a rectangular geometry of CNN patch, then CNN method is used to assign a specific category to this patch, then all pixels within the segment S will be assigned to the same category. Figure 4 shows the standard process of the integration between OBIA and CNN methods, two ideal situations may occur during the process of OBIA-CNN integration, the first situation arises when the segment (S) is larger than CNN patch, the patch is fully contained inside the segment (S), while the second situation arises when the CNN patch is larger than the segment (S) and the patch includes several segments represent the same land cover type, according to these situations, CNN patch is typically consistent with the segment (S), which can improve the accuracy of classification. The problem arises when the CNN patch covers multiple segments with different land cover categories. For example, a segment belongs to a specific type of land cover called (A); however, the CNN decision is mainly influenced by all of the pixels throughout the CNN patch, and the final output will represent the dominant land cover called (B) depending on several factors such as the dominant band value, larger areas, and structured textures. In this case, CNN classifier will make a wrong decision and obtain an incorrect classification for this segment [3]. Figure 4 shows the concept of the integration between OBIA and CNN. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Integration Framework** & **Base Models** & **Description** \\ \hline \hline OBIA ANN & ANN & Applies ANN to OBIA features extracted at the image object level. This framework does not require training a CNN. \\  & & This framework has two computational branches. The first branch extracts OBIA features from the data. The second branch extracts deep features with a CNN from the data. Then, the obtained features are combined. Finally, ANN is applied to learn contextual features and perform classification. \\  & & This framework uses a CNN to obtain an initial classification map. Then, the image is segmented with a segmentation algorithm. Finally, the CNN map is refined based on the segmentation results with majority filtering. \\  & & Patch filtering uses image segmentation results to filter each image patch based on the dominant image object in the image patch. First, it produces image patches and image segmentation. Then, applies a variance filter to each image patch using the results of the segmentation. Finally, a CNN is applied to the obtained filtered image patches. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the integration frameworks assessed in this research. Figure 4: An example of the integration between OBIA and CNN for satellite image classification. * Decision Fusion This type of integration refines a classification map that is produced with a CNN or any other classification method based on the results of image segmentation (Figure 6). A majority filter is applied to make sure that each pixel in the image that belongs to the same image object is classified with the same label. This method is highly dependent on the accuracy of the CNN classification map and the results of the image segmentation. Other types of filtering may also be used, such as median and mean filters instead of the majority filtering. * Patch Filtering Patch filtering frameworks aim to filter image patches based on OBIA segmentation before using them in CNNs (Figure 4).