Water Classification Using Convolutional Neural Network
The classification of water sources is a challenging task due to the low contrast texture features, the visual similarities between them, and the causes posed by image acquisition with different camera angles and placements. The various image enhancement techniques, i.e., Unsharp Masking (UM), Histo...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10190561/ |
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| author | Saira Asghar Ghulam Gilanie Mubbashar Saddique Hafeez Ullah Heba G. Mohamed Irshad Ahmed Abbasi Mohamed Abbas |
| author_facet | Saira Asghar Ghulam Gilanie Mubbashar Saddique Hafeez Ullah Heba G. Mohamed Irshad Ahmed Abbasi Mohamed Abbas |
| author_sort | Saira Asghar |
| collection | DOAJ |
| description | The classification of water sources is a challenging task due to the low contrast texture features, the visual similarities between them, and the causes posed by image acquisition with different camera angles and placements. The various image enhancement techniques, i.e., Unsharp Masking (UM), Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Contrast Stretching, were used to highlight the contrast and texture features of water images. The enhanced image samples were then fed to the proposed Convolutional Neural Network (CNN)-based model named WaterNet (WNet) for classification. From all employed image enhancement techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) provides better results in terms of contrast and texture features of water. CLAHE also improved the classification performance of the proposed model, with an accuracy of 97%. For comparison, experiments have also been performed on state-of-the-art pre-trained models, which are DenseNet-201, Inception_ResNet_v2, Inception_v3, and Mobile-Net. Comparison shows that the proposed technique achieves better accuracy in comparison with the state-of-the-art methods. |
| format | Article |
| id | doaj-art-45d441a0bf1440ac9519d3061b0c96cb |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-45d441a0bf1440ac9519d3061b0c96cb2025-08-20T03:07:01ZengIEEEIEEE Access2169-35362023-01-0111786017861210.1109/ACCESS.2023.329806110190561Water Classification Using Convolutional Neural NetworkSaira Asghar0Ghulam Gilanie1https://orcid.org/0000-0001-6880-8506Mubbashar Saddique2https://orcid.org/0000-0003-4828-5570Hafeez Ullah3https://orcid.org/0000-0003-0640-2628Heba G. Mohamed4https://orcid.org/0000-0002-0443-1049Irshad Ahmed Abbasi5https://orcid.org/0000-0003-1813-1415Mohamed Abbas6Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur Campus, Bahawalpur, PakistanDepartment of Computer Science, The Islamia University of Bahawalpur, Bahawalpur Campus, Bahawalpur, PakistanDepartment of Computer Science and Engineering, University of Engineering and Technology Lahore, Narowal Campus, Narowal, PakistanDepartment of Physics, The Islamia University Bahawalpur, Bahawalpur Campus, Bahawalpur, PakistanDepartment of Electrical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaFaculty of Science and Arts Belqarn, University of Bisha, Sabtul Alaya, Saudi ArabiaElectrical Engineering Department, College of Engineering, King Khalid University, Abha, Saudi ArabiaThe classification of water sources is a challenging task due to the low contrast texture features, the visual similarities between them, and the causes posed by image acquisition with different camera angles and placements. The various image enhancement techniques, i.e., Unsharp Masking (UM), Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Contrast Stretching, were used to highlight the contrast and texture features of water images. The enhanced image samples were then fed to the proposed Convolutional Neural Network (CNN)-based model named WaterNet (WNet) for classification. From all employed image enhancement techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) provides better results in terms of contrast and texture features of water. CLAHE also improved the classification performance of the proposed model, with an accuracy of 97%. For comparison, experiments have also been performed on state-of-the-art pre-trained models, which are DenseNet-201, Inception_ResNet_v2, Inception_v3, and Mobile-Net. Comparison shows that the proposed technique achieves better accuracy in comparison with the state-of-the-art methods.https://ieeexplore.ieee.org/document/10190561/Water sourceswater source classificationwater imagesWaterNet (WNet)image processingimage enhancement techniques |
| spellingShingle | Saira Asghar Ghulam Gilanie Mubbashar Saddique Hafeez Ullah Heba G. Mohamed Irshad Ahmed Abbasi Mohamed Abbas Water Classification Using Convolutional Neural Network IEEE Access Water sources water source classification water images WaterNet (WNet) image processing image enhancement techniques |
| title | Water Classification Using Convolutional Neural Network |
| title_full | Water Classification Using Convolutional Neural Network |
| title_fullStr | Water Classification Using Convolutional Neural Network |
| title_full_unstemmed | Water Classification Using Convolutional Neural Network |
| title_short | Water Classification Using Convolutional Neural Network |
| title_sort | water classification using convolutional neural network |
| topic | Water sources water source classification water images WaterNet (WNet) image processing image enhancement techniques |
| url | https://ieeexplore.ieee.org/document/10190561/ |
| work_keys_str_mv | AT sairaasghar waterclassificationusingconvolutionalneuralnetwork AT ghulamgilanie waterclassificationusingconvolutionalneuralnetwork AT mubbasharsaddique waterclassificationusingconvolutionalneuralnetwork AT hafeezullah waterclassificationusingconvolutionalneuralnetwork AT hebagmohamed waterclassificationusingconvolutionalneuralnetwork AT irshadahmedabbasi waterclassificationusingconvolutionalneuralnetwork AT mohamedabbas waterclassificationusingconvolutionalneuralnetwork |