Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/11/6816 |
| _version_ | 1797597778561466368 |
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| author | Kalupahanage Dilusha Malintha De Silva Hyo Jong Lee |
| author_facet | Kalupahanage Dilusha Malintha De Silva Hyo Jong Lee |
| author_sort | Kalupahanage Dilusha Malintha De Silva |
| collection | DOAJ |
| description | 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 specificness 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. |
| first_indexed | 2024-03-11T03:10:16Z |
| format | Article |
| id | doaj.art-253aca12718a4f9c945ef0f1e01a061c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:10:16Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-253aca12718a4f9c945ef0f1e01a061c2023-11-18T07:37:07ZengMDPI AGApplied Sciences2076-34172023-06-011311681610.3390/app13116816Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined LearningKalupahanage Dilusha Malintha De Silva0Hyo Jong Lee1Division of Computer Science and Engineering, CAIIT, Jeonbuk National University, Jeonju 54896, Republic of KoreaDivision of Computer Science and Engineering, CAIIT, Jeonbuk National University, Jeonju 54896, Republic of KoreaAerial 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 specificness 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.https://www.mdpi.com/2076-3417/13/11/6816semantic segmentationdeep learningaerial imagesimage enhancement |
| spellingShingle | Kalupahanage Dilusha Malintha De Silva Hyo Jong Lee Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning Applied Sciences semantic segmentation deep learning aerial images image enhancement |
| title | Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning |
| title_full | Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning |
| title_fullStr | Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning |
| title_full_unstemmed | Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning |
| title_short | Distorted Aerial Images Semantic Segmentation Method for Software-Based Analog Image Receivers Using Deep Combined Learning |
| title_sort | distorted aerial images semantic segmentation method for software based analog image receivers using deep combined learning |
| topic | semantic segmentation deep learning aerial images image enhancement |
| url | https://www.mdpi.com/2076-3417/13/11/6816 |
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