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Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images.

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Title: Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images.
Authors: Basit, Abdul1 (AUTHOR) abdulbasit@itu.edu.pk, Siddique, Muhammad Adnan1 (AUTHOR) khurram.bhatti@itu.edu.pk, Bhatti, Muhammad Khurram1 (AUTHOR), Sarfraz, Muhammad Saquib2 (AUTHOR) muhammad.sarfraz@kit.edu
Source: Remote Sensing. May2022, Vol. 14 Issue 9, p2085-2085. 18p.
Database: Academic Search Ultimate
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  Data: Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2022, Vol. 14 Issue 9, p2085-2085. 18p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=156874423
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        Value: 10.3390/rs14092085
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      – Code: eng
        Text: English
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      – TitleFull: Comparison of CNNs and Vision Transformers-Based Hybrid Models Using Gradient Profile Loss for Classification of Oil Spills in SAR Images.
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            NameFull: Basit, Abdul
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            NameFull: Siddique, Muhammad Adnan
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            NameFull: Bhatti, Muhammad Khurram
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              Text: May2022
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              Y: 2022
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              Value: 14
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