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Detecting humans from airborne visual and thermal imagery is a fundamentalchallenge for Wilderness Search-and-Rescue (WiSAR) teams, who must perform thisfunction accurately in the face of immense pressure. The ability to fuse thesetwo sensor modalities can potentially reduce the cognitive load on humanoperators and/or improve the effectiveness of computer vision object detectionmodels. However, the fusion task is particularly challenging in the context ofWiSAR due to hardware limitations and extreme environmental factors. This workpresents Misaligned Image Synthesis and Fusion using Information from Thermaland Visual (MISFIT-V), a novel two-pronged unsupervised deep learning approachthat utilizes a Generative Adversarial Network (GAN) and a cross-attentionmechanism to capture the most relevant features from each modality.Experimental results show MISFIT-V offers enhanced robustness againstmisalignment and poor lighting/thermal environmental conditions compared toexisting visual-thermal image fusion methods.

MISFIT-V: Misaligned Image Synthesis and Fusion using Information from Thermal and Visual