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Sequential feature selection for efficient landslide segmentation from multi-spectral data
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DOI：10.3389/frsen.2026.1877713.png)
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Abstract
En 中文
Landslide detection from satellite imagery has advanced through deep learning; yet most models rely on large; highly correlated spectral-topographic inputs whose contributions remain poorly understood. The question of which channels are actually necessary has received surprisingly little attention. This matters: redundant or correlated inputs obscure physical interpretability; inflate computational overhead; and can actively degrade model performance through the Hughes Phenomenon. We present a systematic; explainable channel-selection framework for the Landslide4Sense benchmark; combining Sentinel-2 multispectral and ALOS PALSAR terrain data with 16 engineered spectral and structural indices. Rather than relying on conventional single-band drop tests; which evaluate channels in isolation and miss interaction effects; we apply Sequential Forward Floating Selection (SFFS) to iteratively build and prune a candidate feature pool using a lightweight U-Net++ proxy model. Beyond identifying a compact 8-channel subset that matches or exceeds the segmentation F1 of configurations using up to 30 channels; we use the selection process itself to interrogate which spectral and topographic features landslide models genuinely rely on; and what this reveals about the physical cues driving their predictions. We argue that SFFS represents a principled feature selection approach to input design in Earth observation; in contrast to the prevailing practice of appending every available band and hoping the model learns what to ignore.
Keywords:
deep learning
explainability
sentinel-2
feature selection
channel selection
landslide segmentation
Landslide4Sense
multispectral remote sensing
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