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Visual saliency-based landslide identification using super-resolution remote sensing data
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DOI：10.1016/j.rineng.2023.101656.png)
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摘要
En 中文
Landslides, ubiquitous geological hazards on steep slopes, present formidable challenges in tropical regions with dense rainforest vegetation, impeding accurate mapping and risk assessment. To address this, we propose an innovative deep-learning framework utilizing visual saliency for automatic landslide identification, employing super-resolution remote sensing image datasets. Unlike conventional models relying on raw images, our method leverages saliency-generated feature maps, achieving a remarkable 94% accuracy, surpassing existing models by 5%. Comprehensive experimental findings consistently demonstrate its superiority over established algorithms, highlighting its robust performance. This novel approach introduces a valuable dimension to landslide detection, particularly in complex terrains, offering a promising tool for advancing risk assessment and management in landslide-prone areas.
Keyword:
Deep learning
Visual saliency
Image segmentation
Damage detection
Feature extraction
Remote sensing
Gaussian kernel
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期刊
IF:
7.9
论文数:
1.2W
被引数:
1.7W
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引用论文
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