From Regression Based on Dynamic Filter Network to Pansharpening by Pixel-Dependent Spatial-Detail Injection

Abstract

Compared with hardware upgrading, pansharpening is a low-cost way to acquire high-quality images, which usually combines multispectral images (MS) in low spatial resolution with panchromatic images (PAN) in high spatial resolution. This paper proposes a pixel-dependent spatial-detail injection network (PDSDNet). Based on a dynamic filter network, PDSDNet constructs nonlinear mapping of the simulated panchromatic band from low-resolution multispectral bands through filtering convolution regression. PDSDNet reduces the possibility of spectral distortion and enriches spatial details by improving the similarity between the simulated panchromatic band and the real panchromatic band. Moreover, PDSDNet assumes that if an ideal multispectral image that has the same resolution with the panchromatic image exists, each band of it should have the same spatial details as in the panchromatic image. Thus, the details we fill into each multispectral band are the same and they can be extracted effectively in one pass. Experimental results demonstrate that PDSDNet can generate high-quality fusion images with multispectral images and panchromatic images. Compared with BDSD, MTF-GLP-HPM-PP, and PanNet, which are widely applied on IKONOS, QuickBird, and WorldView-3 datasets, pansharpened images of the proposed method have rich spatial details and present superior visual effects without noticeable spectral and spatial distortion.

Keywords

• pansharpening
• deep learning
• dynamic filter network
• pixel-dependent