Abstract: Visualizing Digital Elevation Models (DEMs) as hillshade maps is a common approach for remote sensing identification of geological hazards. However, conventional hillshade maps, constrained by unidirectional light sources, often lead to misinterpretation or omission of critical geological features during analysis. To address this limitation, this study proposes an enhanced visualization method (termed SOG: Slope-Openness-SVF Group) by integrating three illumination-invariant factors—Sky-View Factor (SVF), Openness, and Slope Gradient—through multi-blending modes into a unified layer. Applied to landslides, collapses, and debris flow identification, the SOG method demonstrates superior visualization enhancement compared to traditional DEM-derived representations (e.g., hillshade, SVF, or RRIM). Edge detection-based feature extraction further reveals that SOG layers capture significantly more micro-topographic characteristics of geological hazards than other visibility layers. The proposed method mitigates key challenges in conventional workflows, including data redundancy, storage inefficiency, operational complexity, and suboptimal visualization outcomes. Thus, SOG offers a novel and effective solution for improving remote sensing-based identification of geological hazards, particularly in micro-terrain feature detection.