###### Abstract  
Aerial images are important for monitoring land cover and land resource management. An aerial imaging source which keeps its position at a higher altitude, and which has a considerable duration of airtime, employs wireless communications for sending images to relevant receivers. An aerial image must be transmitted from the image source to a ground station where it can be stored and analyzed. Due to transmission errors, aerial images which are received from an image transmitter contain distortions which can affect the quality of the images, causing noise, color shifts, and other issues that can impact the accuracy of semantic segmentation and the usefulness of the information contained in the images. Current semantic segmentation methods discard distorted images, which makes the available dataset small or treats them as normal images, which causes poor segmentation results. This paper proposes a deep-learning-based semantic segmentation method for distorted aerial images. For different receivers, distortions occur differently, and by considering the receiver specifications of the distortions, the proposed method was able to grasp the acceptability for a distorted image using semantic segmentation models trained with large aerial image datasets to build a combined model that can effectively segment a distorted aerial image which was received by an analog image receiver. Two combined deep learning models, an approximating model, and a segmentation model were trained combinedly to maximize the segmentation score for distorted images. The results showed that the combined learning method achieves higher intersection-over-union (IoU) scores than the results obtained by using only a segmentation model. semantic segmentation; deep learning; aerial images; image enhancement +
Footnote †: journal: Applied Sciences  
without considering technological details such as image transmission from source to ground stations. The quality of the images depends on the image-capturing mechanism of the source and the image transmission process. Sending an aerial image to a receiving ground station is practically achieved by establishing a wireless communication link between the satellite itself and the ground station. During transmission, radio communications can be disturbed in many ways, such as attenuation and interference. When this happens, inevitably, some information regarding the aerial image will be lost, causing receiver dissatisfaction. Image information which was originally modulated to the radio carrier will not be demodulated or recovered correctly. This will result in distorted images at the receiver's end. Figure 1 shows an example of a source (transmitted) image and the resulting received image. Advancements in deep learning architectures for image classifications and big data analysis have demonstrated the use of such techniques in land cover classification and changed regions detection in aerial images [7]. Labor-intensive tasks, such as the mapping of road networks in aerial images, are becoming inexpensive with deep learning [8]. For damage assessment in timely disaster risk management, an adequate deep-learning-based framework has been proposed [9]. Many aerial image datasets, such as [10], are presented with fine annotations and readily available for semantic segmentation. Images and their corresponding annotations are applicable for popular deep learning segmentation models such as U-net [11] and Linknet [12]. However, in the case of certain transmission systems, consideration of the uncertainty of having some distorted aerial images to be semantically segmented and the results of such images with existing pretrained semantic segmentation models will not be better than images with no distortions. Another challenging fact is the lack of distorted aerial images to train a segmentation model. The manufacturing process of satellite image receivers is still unable to properly solve the problem of distortions in received images. Existing distortions have made it difficult to correctly examine aerial data.