## 6 Conclusions  
In this paper, we proposed an image segmentation method for distorted aerial images, which can be used in software-based aerial images reception and analyzing processes. Depending on an individual or an organization, methods of communication differ according to the scale of materials to be transmitted and received. In image communications, a variety of image receivers have suffered distortions in received contents. With a few hundred available distorted aerial images, the proposed method gained the advantage of using a segmentation model which was trained with thousands of aerial images and segmentation labels. Without a need for training a segmentation model, the added approximator model has taken the obligation of presenting a proper input to a pre-trained segmentation model. The proposed method employed a modular approach so that different combinations of approximator models and segmentation models can be used. Deep learning models which are yet to be introduced can be used as modules, hence perfectly futureproofing the software and hardware compatibility for receivers to use the proposed method. For controller software with a GUI, users can choose their preferred models from a list. The results obtained by using the proposed method showed a significant improvement in the semantic segmentation of distorted aerial images. For future works, we will increase the distorted aerial image dataset by performing more image transmitting activities and expand the concept into the general image communications over radio-waves-related applications and for satellite images from other planets. It is a transformation to receive images from much larger standards of image transmitting methods and to test different software-based image receiver hardware for seeking more implementations for the proposed combined learning method.  
[PERSON] designed and developed the proposed method, conducted the experiments, and wrote the manuscript. [PERSON] designed the new concept, provided the conceptual idea and insightful suggestions to refine it further, and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by project for Joint Demand Technology R&D of Regional SMEs funded by Korea Ministry of SMEs and Startups in 2023 (RS-2023-00207672).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segmentation Model** & **Backbone** & \
\begin{tabular}{c} **Parameters in** \\ **Combined Form (M)** \\ \end{tabular} &
\begin{tabular}{c} **Evaluation Time** \\ **(ms/batch)** \\ \end{tabular} \\ \hline Unet & Resnet18 & 14.75 & 288 \\  & SeResnet18 & 14.84 & 286 \\  & DenseNet121 & 12.55 & 306 \\  & InceptionV3 & 30.34 & 316 \\  & MobileNetV2 & 8.46 & 288 \\  & EfficientNetB0 & 10.52 & 283 \\ \hline Linknet & Resnet18 & 11.93 & 266 \\  & SeResnet18 & 12.02 & 275 \\  & DenseNet121 & 8.76 & 294 \\  & InceptionV3 & 26.68 & 311 \\  & MobileNetV2 & 4.55 & 290 \\  & EfficientNetB0 & 6.50 & 305 \\ \hline FPN & InceptionV3 & 25.44 & 367 \\  & MobileNetV2 & 5.62 & 342 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and computational complexity of combined networks.