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Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24–25, 2023, Proceedings

Research Article

A Systematic Review: Remote Sensed Hyperspectral Image Segmentation and Caption Generation Using Deep Learning Methods

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  • @INPROCEEDINGS{10.1007/978-3-031-66044-3_3,
        author={Namdeo Baban Badhe and Vinayak Ashok Bharadi and Nupur Giri and Sujata Alegavi and Vijaykumar Yele},
        title={A Systematic Review: Remote Sensed Hyperspectral Image Segmentation and Caption Generation Using Deep Learning Methods},
        proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24--25, 2023, Proceedings},
        proceedings_a={PERSOM},
        year={2024},
        month={8},
        keywords={Hyperspectral image segmentation remote sensing image captioning deep learning feature extraction attention mechanism},
        doi={10.1007/978-3-031-66044-3_3}
    }
    
  • Namdeo Baban Badhe
    Vinayak Ashok Bharadi
    Nupur Giri
    Sujata Alegavi
    Vijaykumar Yele
    Year: 2024
    A Systematic Review: Remote Sensed Hyperspectral Image Segmentation and Caption Generation Using Deep Learning Methods
    PERSOM
    Springer
    DOI: 10.1007/978-3-031-66044-3_3
Namdeo Baban Badhe1,*, Vinayak Ashok Bharadi1, Nupur Giri2, Sujata Alegavi3, Vijaykumar Yele4
  • 1: Department of Information Technology, Finolex Academy of Management and Technology, P-60, P-60/1, Midc, Mirjole Block, Ratnagiri
  • 2: Department of Computer Engineering, Vivekanand Education Society’s Institute of Technology, Hashu Adwani Memorial Complex, Collector’s Colony, Mumbai
  • 3: Head of the BTech Internet of Things Department, Thakur College Engineering and Technology, Kandivali - (East)
  • 4: Electronics and Telecommunication Engineering, Thakur College Engineering and Technology, Kandivali - (East)
*Contact email: namdeobadhe1982@gmail.com

Abstract

Hyperspectral images (HSIs) exhibit a high-dimensional nature, capturing data across numerous wavelengths in the electromagnetic spectrum, often spanning thousands of bands. It has found widespread applications in various real-life scenarios due to its ability to leverage the rich spectral information contained within each pixel. Deep Learning (DL) schemes offer a huge variety of chances to resolve traditional imaging tasks and also for approaching various simulating issues in the spatial-spectral region. This review work provides a systematic review of the relevant existing techniques based on HSI segmentation and image captioning. Initially, other DL methods like, Deep Belief Network (DBN), Convolutional Neural Network (CNN), Autoencoders, Fully Convolutional Neural Network (FCNN), UNet, and Graph Convolutional Network (GCN) are discussed. Secondly, a significant computer vision problem that has recently evolved is image captioning, which tries to automatically produce English explanations of an input image. Therefore, image captioning has garnered growing interest within the realm of remote sensing. This survey summarizes the relevant methods and concentrates on the feature extraction-based methods and attention mechanism-based techniques, which plays a significant role in image caption generation tasks. Finally, it provides the research gaps and its appropriate solution at the end of each survey.

Keywords
Hyperspectral image segmentation remote sensing image captioning deep learning feature extraction attention mechanism
Published
2024-08-13
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-66044-3_3
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