Pansharpening with multi-CAE: impact of patch size and overlapping pixels on spectral and spatial distortion

Abstract

A novel technique utilizing a convolutional autoencoder (CAE) is introduced with the aim of enhancing the spatial resolution of multispectral (MS) images while concurrently mitigating spectral distortion. First, an original panchromatic (PAN) image is constructed from its spatially degraded version. Then, the relationship between the original PAN image and its degraded version is utilized to reconstruct the high-resolution MS image; in addition, an intensity component of MS image, which is obtained using an adaptive intensity-hue-saturation (AIHS), is reconstructed by utilizing the aforementioned relationship. Two types of remote sensing datasets are adopted, and the effect of the patch size with the overlapping pixel on spectral and spatial distortion is considered. After training CAE, the low-resolution MS image and its intensity component are given to the trained network as input to obtain the MS image and intensity component with better details. Eventually, the fused image is obtained by using a component substitution (CS) framework. Experimental findings corroborate that the proposed method yields superior outcomes compared with several existing approaches, demonstrating advantages in both objective metrics and visual fidelity.