Differential Analysis of Multispectral Images for Terrain Identification

• URL: http://arxiv.org/abs/2607.09319v1
• Date: Fri, 10 Jul 2026 11:57:46 GMT
• Title: Differential Analysis of Multispectral Images for Terrain Identification
• Abstract summary: DRIFT is a lightweight multispectral framework that combines raw spectral bands and illumination-tolerant band-ratio representations.<n>We evaluate DRIFT on a new oil-on-soil multispectral dataset acquired using a MicaSense RedEdge-P camera mounted on an Unmanned Aerial Vehicle.
• Score: 2.1133150522532493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable terrain understanding is a prerequisite for autonomous robot navigation. Yet, the widespread RGB-based perception can fail under low illumination, shadows, and material ambiguities. In this work we propose DRIFT, a lightweight multispectral framework that combines raw spectral bands and illumination-tolerant band-ratio representations through a dual-stream residual architecture and a differential fusion branch. Band ratios attenuate multiplicative acquisition effects (illumination/sensor gains), while the differential fusion explicitly highlights discrepancies between absolute-band and ratio-derived cues, which improves the robustness to noisy or partially unreliable spectral measurements. In the paper (i) we evaluate DRIFT on a new oil-on-soil multispectral dataset acquired using a MicaSense RedEdge-P camera mounted on an Unmanned Aerial Vehicle, and (ii) we provide an additional controlled study on water-on-grass under varying illumination and thermal perturbations (hot/cold water) to analyze NIR-sensitive effects. DRIFT consistently improves over strong baselines, while remaining compatible with edge deployment.

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