Publication Type : Conference Paper
Publisher : IEEE
Source : 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)
Url : https://ieeexplore.ieee.org/abstract/document/10116882
Campus : Coimbatore
School : School of Artificial Intelligence - Coimbatore
Year : 2023
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.
Cite this Research Publication : Sunandini, Gosula, Ramesh Sivanpillai, V. Sowmya, and VV Sajith Variyar. "Significance of Atrous Spatial Pyramid Pooling (ASPP) in Deeplabv3+ for Water Body Segmentation." In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 744-749. IEEE, 2023.