CLIM: Co-occurrence with Laplacian Intensity Modulation and Enhanced Color Space Transform for Infrared-Visible Image Fusion

Thermal infrared and multispectral visible remote sensing image fusion combines thermal image information with corresponding visible scene content to generate a better representative fused image. Thermal images can distinguish targets using difference in thermal radiation measurements, whereas visible images contain better texture detail in multispectral wavelength bands. The article presents a novel methodology named CLIM to sharpen coarser spatial resolution multispectral remote sensing images using relatively higher spatial resolution broadband thermal infrared image. The boundary-preserving information is extracted from high resolution thermal infrared image using co-occurrence image filter, and is combined with Laplacian of Gaussian based sharpened image to extract salient features for injection. In addition, visible image is transformed to IHS color space, and intensity component is enhanced using CLAHE and inverse transformation to generate enhanced visible image for fusion. The procedure developed is evaluated with Indian Nano Satellite (INS) broadband thermal infrared images available at a spatial resolution of 175 m with same day acquisition MODIS multispectral visible images available at a relatively coarser spatial resolution of 500 m. The nearest acquisition of Landsat-8 thermal infrared images with MODIS multispectral visible images is also used for infrared-visible multi-modal image fusion. The CLIM fused image confirms that distinct features such as dam, ship docking zones and refinery regions, are better demarked and semantically more meaningful in comparison with individual thermal infrared and multispectral visible image. The proposed CLIM approach is compared with, and found to perform better than state-of-the-art image fusion techniques, both visually and quantitatively.