An object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery
This study presents an object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery. The proposed approach integrates time-series multispectral image data with digital elevation models to enhance the robustness and accuracy of landslide detec...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2025.2471014 |
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| Summary: | This study presents an object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery. The proposed approach integrates time-series multispectral image data with digital elevation models to enhance the robustness and accuracy of landslide detection. The methodology employs multiresolution segmentation with a fusion of spectral and topographic features, enabling the model to capture complex patterns and improve segmentation quality. The study used the multivariate long short-term memory fully convolutional network (LSTM-FCN) architecture to process the multivariate time-series data, significantly enhancing landslide detection performance. The experimental results demonstrated that the fused model outperformed models that used only spectral data, achieving higher accuracy and reducing commission and omission errors. Furthermore, the generalization capability of the model was validated on an independent test site, showcasing its potential applicability in diverse geographical contexts. Integrating diverse data sources is essential for accurate landslide detection and can offer valuable insights to facilitate disaster management and mitigation efforts. |
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| ISSN: | 1947-5705 1947-5713 |