| Abstract |
Water body segmentation is a difficult task because of complex shapes, edges, sizes, and the surroundings. DeeplabV3+ has proven to perform well on segmentation tasks with multi-scale features. Atrous Spatial Pyramid Pooling (ASPP) module present in Deeplab models helps to increase the field of view for extracting multi-scale features. In this work, we analyze how the ASPP extracts multi-scale features in DeeplabV3+ by comparing the performance of Deeplabv3+ model with ASPP and without ASPP for water segmentation task. The dataset used for this study is a public dataset that contains RGB satellite images and corresponding masks from sentine1-2 A/B satellite. The evaluation metrics used for this study are IoU score, Dice score, Recall and Precision. The results obtained from the study shows that DeeplabV3+ model with ASPP extract the water bodies with different sizes. It also captures the boundaries accurately compared to the model without ASPP module. |
| Authors |
Gosula Sunandini , Ramesh Sivanpillai   , V. Sowmya , V. V. Sajith Variyar
|
| Journal Info |
Not listed | 2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)
|
| Publication Date |
3/23/2023 |
| ISSN |
Not listed |
Type |
article |
| Open Access |
closed
|
| DOI |
https://doi.org/10.1109/spin57001.2023.10116882 |
| Keywords |
Surface Water Mapping (Score: 0.551573) , Hydrological Modeling (Score: 0.513958)
|
