Spatio-temporal analysis of land use/land cover change detection in small regions using self-supervised lightweight deep learning

Naik, Nitesh; Chandrasekaran, Kandasamy; Meenakshi Sundaram, Venkatesan; Panneer, Prabhavathy

doi:10.1007/s00477-023-02554-6

Cited by 3 publications

(1 citation statement)

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“…With the development of deep learning, Nitesh Naik proposed the use of self-supervised learning to detect land changes. This method combines superpixel segmentation, difference image generation, feature extraction, clustering, deep learning classification, and image merging to achieve land cover change detection [124]. Chi Zhang introduced a fully Atrous convolutional neural network (FACNN) for land cover classification.…”

Section: Unsupervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

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Section: Unsupervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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Hierarchical homogeneity-based superpixel segmentation: application to hyperspectral image analysis

Ayres,

de Almeida,

Bermudez

et al. 2024

International Journal of Remote Sensing

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Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

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Change Vector Change Indices Method for Unsupervised Remote Sensing Change Detection

Sundaram,

Prabhavathy

2024

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)

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Spatio-temporal analysis of land use/land cover change detection in small regions using self-supervised lightweight deep learning

Cited by 3 publications

References 50 publications

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Hierarchical homogeneity-based superpixel segmentation: application to hyperspectral image analysis

Change Vector Change Indices Method for Unsupervised Remote Sensing Change Detection

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