Exploring the Applicability of Spectral Recovery in Semantic Segmentation of RGB Images

Compared with RGB images, hyperspectral images (HSIs) offer a distinct advantage in that they can record continuous spectral bands of light reflectance in each pixel, reflecting the physical and chemical characteristics of materials. This capability enables differentiation between objects that may have similar textures but different spectral characteristics. It is desirable to recover spectral information from RGB images to improve semantic segmentation accuracy. Additionally, semantic information can serve as a guide for spectral information recovery, thereby ensuring the quality of the recovered spectral information. The two tasks are mutually beneficial in this regard. In light of these considerations, we propose a multi-task framework that exploits the complementary relationship between spectral recovery and semantic segmentation tasks, comprising a complementary spectral-semantic attentive fusion model (CSSF) that enables the two tasks to mutually facilitate each other by fusing information from both branches. Specifically, the proposed CSSF incorporates a window-based spectral-semantic attentive fusion (WSSAF) module to incorporate recovered spectral information into the segmentation process effectively, and a pixel-shuffle-based fusion (PSF) module to provide semantic guidance for spectral recovery. To evaluate the effectiveness of our approach, we built the first flower hyperspectral image dataset (FHRS) with corresponding segmentation annotations and RGB images. By doing so, we have made the first attempt to explore the complementary relationship between semantic segmentation and spectral recovery. Experimental results on both the FHRS dataset and the publicly available LIB-HSI dataset demonstrate that our proposed method has the ability to enhance both tasks by utilizing their complementary relationship, indicating the generalization ability of our method.