Semantic Segmentation of Landsat Satellite Imagery

Currently, practitioners of remote-sensing and geographic information system use mathematical and statistical-based methods in making land cover segmentation. However, this method requires a lengthy process and staff expertise. Urban planners need applications that are not only fast in performing sp...

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Bibliographic Details
Published in:2022 Seventh International Conference on Informatics and Computing (ICIC) pp. 1 - 6
Main Authors: Herlawati, Herlawati, Handayanto, Rahmadya Trias, Atika, Prima Dina, Sugiyatno, Sugiyatno, Rasim, Rasim, Mugiarso, Mugiarso, Hendharsetiawan, Andy Achmad, Jaja, Jaja, Purwanti, Santi
Format: Conference Proceeding
Language:English
Published: IEEE 08-12-2022
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Summary:Currently, practitioners of remote-sensing and geographic information system use mathematical and statistical-based methods in making land cover segmentation. However, this method requires a lengthy process and staff expertise. Urban planners need applications that are not only fast in performing spatial analysis but also do not require special skills. This study proposes a land cover classification application with one of the deep learning methods for semantic segmentation, namely DeepLabV3+. This method will be compared with Iterative Self-Organizing Clustering (ISOCLUST) and Object Based Image Analysis (OBIA) in Karawang, Indonesia, as the case study. The results showed that the DeepLabV3+ accuracy was 95% which is higher than OBIA (80%). Although ISOCLUST more accurate that is usually used as ground truth dataset, this method takes a lot of time as it is semi-automatic compared to DeepLabV3+ which only takes about one minute.
DOI:10.1109/ICIC56845.2022.10006917