Spectral-Spatial Interpretation

Key points

• Post-hoc image explanations usually show where a model looks using spatial heatmaps or indicate which channels matter using global channel scores.1
• These separate views are limiting for multispectral data because channel relevance is often spatially localized and physically meaningful.1
• The result is a full spectral-spatial attribution matrix rather than a single heatmap or a global channel ranking.1
• Across CUB-200, EuroSAT, and HLS Burn Scar under both per-channel mean and zero baselines, SSI achieves the best joint deletion and insertion AUC over dense, flat, and rank-1 baselines, yields the strongest spectral hit rates and channel deletion, and leads spatial localization on both annotated datasets.1

Sources (1)

• [1]Spectral-Spatial Interpretation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 02:17 AM

  We introduce Spectral-Spatial Interpretation (SSI), a full-grid attribution method over position-channel players (g,c).

  Post-hoc image explanations usually show where a model looks using spatial heatmaps or indicate which channels matter using global channel scores.

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