###### Abstract  
During the past decade, deep learning-based classification methods (e.g., convolutional neural networks--CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targets of interest and do not extract geometric features such as shape and area. Previous research has attempted to address such issues by combining deep learning with methods such as object-based image analysis (OBIA). Nonetheless, the question of how to integrate those methods into a single framework in such a way that the benefits of each method complement each other remains. To that end, this study compared four integration frameworks in terms of accuracy, namely OBIA artificial neural network (OBIA ANN), feature fusion, decision fusion, and patch filtering, according to the results. Patch filtering achieved 0.917 OA, whereas decision fusion and feature fusion achieved 0.862 OA and 0.860 OA, respectively. The integration of CNN and OBIA can improve classification accuracy; however, the integration framework plays a significant role in this. Future research should focus on optimizing the existing CNN and OBIA frameworks in terms of architecture, as well as investigate how CNN models should use OBIA outputs for feature extraction and classification of remotely sensed images. deep learning; convolutional neural networks; Object-Based Image Analysis; remote sensing; integration frameworks +
Footnote †: journal: Medical Image Analysis  
CNNs have excellent capabilities in extracting useful abstract features from remote sensing images; however, they are not directly applicable for performing classification at the object level. CNNs require image patches as input data to make inferences on a pixel's category label. This data structure is prone to errors when classifying the boundaries of ground objects [3] ([PERSON] and [PERSON] 2019). Furthermore, the rectangular patch of CNNs is scarcely consistent with the non-homogeneous segment obtained from the OBIA process. Therefore, when applying CNN classifier on a segment at the boundary of a specified land cover, the CNN patch that includes the segment that is to be classified may also contain other segments belonging to a different type of land cover. In addition, these segments that belonged to other land cover categories may exhibit higher band values or notable patterns and texture. As a consequence, they will significantly influence the decision of CNNs. As a result of this inconsistency, classification errors will occur when we directly train the CNNs model on segments resulting from the OBIA method. This kind of error is called a jagged error, which will be seen between land cover borders as a shrink or over-expand forms in the resulting image. This issue has severely restricted the application of CNNs in OBIA classification. Generally, patch-based or pixel-level CNNs methods are not effectively designed respecting computational efficiency or precision to handle the complicated challenges of the classification of remotely sensed images [2] ([PERSON] 2018). Unlike using deep learning on image pixels or patches, its use with OBIA is relatively more complex because of the data transformation requirements to specific data structures that deep learning models can be trained on. The literature contains four main frameworks that can be used to combine deep learning and OBIA, including (1) extracting deep and abstract features from OBIA attributes with deep learning, (2) OBIA-deep learning features fusion, (3) decision level fusion, and (4) heterogeneous segments filtering. While there are many research papers on the integration of CNNs and OBIA for remote sensing image classification, there is a significant lack of understanding of how these integration frameworks compare. Thus, this research aims to present a comparison of four common frameworks used to combine CNNs and OBIA for image classification.