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Patch-wise semantic segmentation of sedimentation from high-resolution satellite images using deep learning
Pranto, T.H.; Noman, A.A.; Noor, A.; Deepty, U.H.; Rahman, R.M. (2021). Patch-wise semantic segmentation of sedimentation from high-resolution satellite images using deep learning, in: Rojas, I. et al. Advances in computational intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Lecture Notes in Computer Science, 12861: pp. 498-509. https://dx.doi.org/10.1007/978-3-030-85030-2_41
In: Rojas, I.; Joya, G.; Catala, A. (Ed.) (2021). Advances in computational intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Lecture Notes in Computer Science, 12861. Springer Nature Switzerland AG: Cham. e-ISBN 978-3-030-85030-2. XXII, 624 pp. https://dx.doi.org/10.1007/978-3-030-85030-2, more
In: Lecture Notes in Computer Science. Springer-Verlag: Heidelberg; Berlin. ISSN 0302-9743; e-ISSN 1611-3349, more
Peer reviewed article  

Available in  Authors 

Author keywords
    Multi-class semantic segmentation, Patch-wise learning, U-Net, High resolution satellite image

Authors  Top 
  • Pranto, T.H.
  • Noman, A.A.
  • Noor, A.
  • Deepty, U.H.
  • Rahman, R.M.

Abstract
    n recent times, satellite data availability has increased significantly, helping researchers worldwide to explore, analyze and approach different problems using the most recent techniques. The segmentation of sediment load in coastal areas using satellite imagery can be considered as a cost-efficient process as sediment load analysis can be costly and time-consuming if done hands on. In this work, we created dataset of Bangladesh marine area for segmenting sediment load and showed the applicability of deep learning technique to segment sedimentation into 5 different classes (Land, Hight Sediment, Moderate Sediment, Low Sediment and No Sediment) using deep neural network called U-Net. As our collected satellite image is enormous, we showed how patch-wise learning technique can be an effective solution in the context of batch-wise training. Highest dice coefficient of 86% and validation dice coefficient of 87% has been acquired for Dec-2019 time frame data. The highest 77% of pixel accuracy and 78% of validation pixel accuracy was achieved on the same time frame data.

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