Research Information System
IEEE Geoscience and Remote Sensing Letters, pp.1-5, 2025 (SCI-Expanded)
Multispectral homogeneous bands capture distinct and complementary spectral characteristics, therefore, fusing multiple bands has the potential to increase semantic segmentation performance. However, the fusion of highly correlated homogeneous bands (i.e. RGB, NIR, and SWIR) remains underexplored. We hypothesized that using correlation representations between highly correlated homogeneous spectral bands at higher‑level feature stages may improve segmentation accuracy. Therefore, we propose a novel semantic segmentation architecture that combines homogeneous modalities with a shared latent representation that exploits their intrinsic correlations. We also introduce interactive feature fusion blocks at early encoder stages to extract better cross-band correlations. Our experiments on two different remote sensing image sets, both UAV-based and satellite-based, show that our correlation‑driven fusion among homogeneous bands can enhance segmentation accuracy over state-of-the-art unimodal and multimodal models. Our code is available at: https://github.com/iremulku/CorrIFNet.