## 4 Results and Discussions
### Discussions  
Deep learning methods such as CNNs are efficient feature extractors and have shown to be very successful for many computer vision applications, including image classification. In remote sensing, CNNs also achieved significant results compared to the traditional classification methods due to their ability to extract abstract features both from the spectral and the spatial domains. However, the challenges with deep learning, as presented in previous studies, are the problems of the artifacts in the class boundaries and the salt-and-pepper effect. Integration of OBIA and deep learning can solve the mentioned problems. However, the integration of OBIA into deep learning is not straightforward. This research assessed four common integration frameworks, namely, OBIA ANN, feature fusion, decision fusion, and patch filtering, for the classification of aerial photographs. Figure 10: Classification maps obtained for the test area with the random OCNN and decision fusion. Figure 9: Classification maps obtained for the training area with the random OCNN and decision fusion. In deep learning models, pixels that belong to the same object are not enforced to be classified as the same semantic category. This reduces the artifacts in the class boundaries and the salt-and-pepper effect. However, deep learning has great capability in learning abstract features that are generalizable to new areas. It is; therefore, deep learning-based classification models often achieve high overall accuracy. On the other hand, OBIA, through segmentation, offers classification outputs that have few artifacts in the class boundaries and less salt-and-pepper effect. The combination of two methods can complement the strength of each method and result in classification models that have high accuracy and suitable quality. The integration frameworks assessed in this research each attempt to combine the strength of each method differently. The OBIA ANN, for example, combines the strengths of OBIA, i.e., semantic segmentation and providing detailed spatial and textural features and the ability of ANN to learn contextual features such that the final models have an understanding of the relationship between the features. The main limitation of this approach is that ANN and similar models are not suitable for tabular data. The feature fusion method aims to extract features from each method and use the combined features for classification. This method can lead to redundant information as OBIA, and deep learning may learn the same spatial and textural features from the image data. In order to avoid the negative effects of redundant information in these models, dimensionality reduction techniques may be necessary. Alternatively, adding more layers with strong regularization after the concatenation layer combines the features from the OBIA and deep learning methods. Decision fusion uses only segmentation from OBIA and features from the deep learning method. Thus, there are no impacts of redundant information. However, some features of OBIA that may be useful for classification tasks are neglected. Decision fusion methods are easier to compute and optimize compared to the feature fusion method as there is no calculation of OBIA features. Finally, patch filtering methods are complex due to the requirement of matching between image objects and image patches. Moreover, there are several assumptions and abstractions in this method due to the matching process of the image objects and image patches. In addition, patch filtering methods not utilizing OBIA features as the decision fusion methods. This method uses local semantics based on image patches that may not take full advantage of the OBIA segmentation. This research presented the advantages of combining OBIA and deep learning for very high-resolution satellite classification. Results showed that integrated frameworks, especially patch filtering, had a better result than the CNN-only methods and pixel-based ANN.