SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection
Hyperspectral imaging (HSI) has evolved as an important tool for many applications, including remote sensing, crime investigation, target detection, disease diagnosis, and anomaly detection. Among these, hyperspectral underwater target detection (HUTD) presents unique challenges due to spectral dist...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11075652/ |
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| author | Suresh Aala Sravan Kumar Sikhakolli Sunil Chinnadurai Anuj Deshpande Karthikeyan Elumalai Md. Abdul Latif Sarker Hala Mostafa |
| author_facet | Suresh Aala Sravan Kumar Sikhakolli Sunil Chinnadurai Anuj Deshpande Karthikeyan Elumalai Md. Abdul Latif Sarker Hala Mostafa |
| author_sort | Suresh Aala |
| collection | DOAJ |
| description | Hyperspectral imaging (HSI) has evolved as an important tool for many applications, including remote sensing, crime investigation, target detection, disease diagnosis, and anomaly detection. Among these, hyperspectral underwater target detection (HUTD) presents unique challenges due to spectral distortions caused by water absorption and scattering. Conventional methods often struggle with spectral variability, complicating detection accuracy. This paper introduces spectral variability-aware hybrid autoencoder (SVHAE) for HUTD, a novel autoencoder-based unmixing network incorporating parallel linear and nonlinear decoders to improve underwater target detection. Our method effectively reduces the effect of spectral distortions and addresses variability using a combined loss function integrating Kullback-Leibler divergence, mean squared error, and spectral angle distance. Experimental validation on real-world and simulated datasets demonstrates that our proposed SVHAE outperformed state-of-the-art methods by achieving superior AUC values. These advancements contribute to the progressing field of HUTD, making the way for robust solutions in marine exploration and detecting targets under the water. |
| format | Article |
| id | doaj-art-a92895e00cf54e1eaa29e02159018e8a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a92895e00cf54e1eaa29e02159018e8a2025-08-20T03:14:02ZengIEEEIEEE Access2169-35362025-01-011312916112917110.1109/ACCESS.2025.358743811075652SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target DetectionSuresh Aala0https://orcid.org/0000-0002-3110-5062Sravan Kumar Sikhakolli1https://orcid.org/0000-0002-4887-997XSunil Chinnadurai2https://orcid.org/0000-0002-7464-1578Anuj Deshpande3https://orcid.org/0000-0002-7415-1691Karthikeyan Elumalai4Md. Abdul Latif Sarker5https://orcid.org/0000-0001-7911-3689Hala Mostafa6https://orcid.org/0000-0002-7388-8990Department of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, IndiaDepartment of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, IndiaDepartment of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, IndiaDepartment of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, IndiaDepartment of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati, IndiaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South KoreaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaHyperspectral imaging (HSI) has evolved as an important tool for many applications, including remote sensing, crime investigation, target detection, disease diagnosis, and anomaly detection. Among these, hyperspectral underwater target detection (HUTD) presents unique challenges due to spectral distortions caused by water absorption and scattering. Conventional methods often struggle with spectral variability, complicating detection accuracy. This paper introduces spectral variability-aware hybrid autoencoder (SVHAE) for HUTD, a novel autoencoder-based unmixing network incorporating parallel linear and nonlinear decoders to improve underwater target detection. Our method effectively reduces the effect of spectral distortions and addresses variability using a combined loss function integrating Kullback-Leibler divergence, mean squared error, and spectral angle distance. Experimental validation on real-world and simulated datasets demonstrates that our proposed SVHAE outperformed state-of-the-art methods by achieving superior AUC values. These advancements contribute to the progressing field of HUTD, making the way for robust solutions in marine exploration and detecting targets under the water.https://ieeexplore.ieee.org/document/11075652/Hyperspectral imagingunderwater target detectionparallel spectral unmixinglinear unmixingnonlinear unmixingremote sensing |
| spellingShingle | Suresh Aala Sravan Kumar Sikhakolli Sunil Chinnadurai Anuj Deshpande Karthikeyan Elumalai Md. Abdul Latif Sarker Hala Mostafa SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection IEEE Access Hyperspectral imaging underwater target detection parallel spectral unmixing linear unmixing nonlinear unmixing remote sensing |
| title | SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection |
| title_full | SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection |
| title_fullStr | SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection |
| title_full_unstemmed | SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection |
| title_short | SVHAE: Spectral Variability-Aware Hybrid Autoencoder for Hyperspectral Underwater Target Detection |
| title_sort | svhae spectral variability aware hybrid autoencoder for hyperspectral underwater target detection |
| topic | Hyperspectral imaging underwater target detection parallel spectral unmixing linear unmixing nonlinear unmixing remote sensing |
| url | https://ieeexplore.ieee.org/document/11075652/ |
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