Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning

SAI ESWAR GUDE; DODDI HARSHITH; Aravapalli Nithin; S.Revathy

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Submission Last date	28-Jun-2025
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Authors

Title: :  Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning
PaperId: :  23713
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-23713
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Name	Author Institute
SAI ESWAR GUDE	SRM Institute of Science and Technology
DODDI HARSHITH	SRM Institute of Science and Technology
Aravapalli Nithin	SRM Institute of Science and Technology
S.Revathy	SRM Institute of Science and Technology

Abstract

Research Area	Computer Science and Engineering
KeyWord	Algal blooms, remote sensing, , random forest, extreme gradient boosting, artificial neural network, robustness, comparative analysis.
Abstract	The detection of algal blooms in lakes and reservoirs via remote sensing presents a significant environmental challenge, impacting aquatic ecosystems and human health. While conventional algorithms relying on remote sensing reflectance have shown effectiveness in certain contexts, achieving high accuracy across multiple lakes remains a challenge, particularly with single- threshold-based approaches. This study investigates the performance of various machine learning (ML) algorithms for pinpointing algal bloom locations using Sentinel-2 images in Chinese eutrophic inland lakes. Through comprehensive testing of four ML models - random forest (RF), extreme gradient boosting, artificial neural network, and support vector machine - in lakes Taihu, Chaohu, and Dianchi, alongside index-based methods such as the floating algae index, this research provides insights into their accuracy, stability, and robustness. Results indicate that the RF model exhibits superior performance compared to other ML models, maintaining an overall accuracy above 0.90 across various lakes. Notably, even when trained on data from a single lake, the RF model achieves a commendable accuracy of 0.88 for other lakes. In summary, this comparative analysis underscores the promising potential of ML techniques in enhancing the detection of algal blooms in diverse remote sensing scenarios.

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IJARIIE	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy. "Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 1267-1278
MLA	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy. "Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 1267-1278.
APA	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, & S.Revathy. (2024). Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning. International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 1267-1278.
Chicago	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy. "Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 1267-1278.
Oxford	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy. 'Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 1267-1278. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Universal_Robust_Domain_Adaptation_for_Remote_Sensing_Image_Classification_using_Deep_Optimized_Transfer_Learning_ijariie23713.pdf (Accessed : 14 May 2024).
Harvard	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy. (2024) 'Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 1267-1278IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Universal_Robust_Domain_Adaptation_for_Remote_Sensing_Image_Classification_using_Deep_Optimized_Transfer_Learning_ijariie23713.pdf (Accessed : 14 May 2024)
IEEE	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy, "Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 1267-1278, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Universal_Robust_Domain_Adaptation_for_Remote_Sensing_Image_Classification_using_Deep_Optimized_Transfer_Learning_ijariie23713.pdf [Accessed : 14 May 2024].
Turabian	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy. "Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 (14 May 2024).
Vancouver	SAI ESWAR GUDE, DODDI HARSHITH, Aravapalli Nithin, and S.Revathy. Universal Robust Domain Adaptation for Remote Sensing Image Classification using Deep Optimized Transfer Learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 14 May 2024]; 10(3) : 1267-1278. Available from: https://ijariie.com/AdminUploadPdf/Universal_Robust_Domain_Adaptation_for_Remote_Sensing_Image_Classification_using_Deep_Optimized_Transfer_Learning_ijariie23713.pdf

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