| Autor: |
P. Anilkumar, P. Venugopal, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Amal Al-Rasheed, Mohamed Abbas, Ben Othman Soufiene |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 106688-106705 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3318867 |
| Popis: |
Semantic segmentation is a significant task in the field of Remote Sensing and Computer Vision. DeepLabV3+ is a convolutional neural network architecture that excels in the task of semantic segmentation, which involves assigning a class label to each pixel in an input image. This Paper proposes an Adaptive Deeplabv3+ model for semantic segmentation of Aerial Images, which combines Deeplabv3+ with the Improved Golden Eagle Optimization Algorithm (IGEO), to solve imprecise target segmentation and poor border segmentation accuracy. To enhance the quality of segmentation, Adaptive DeepLabV3+ employs atrous spatial pyramid pooling (ASPP) with multiple dilation rates in the encoder and allows the model to capture multi-scale context information efficiently, enabling it to distinguish between objects with varying scales. The proposed model effectively segmented the aerial images by optimizing the hyper-parameters such as hidden neuron count and learning rate. The suggested model achieved 98.46% accuracy, 96.32% correlation, 96.48% precision and 98.36% dice coefficient within the computation time of 136.8912 and 147.2684 seconds for dataset 1 and 2 respectively. Therefore the evolutional outcomes of the proposed model show significantly improved than the state-of-the-art techniques. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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