High-Low-Frequency Progressive-Guided Diffusion Model for PAN and MS Classification

With the rapid development of remote sensing technology, satellites can easily acquire multispectral (MS) and panchromatic (PAN) images. It is challenging to utilize their complementarity to effectively combine each other’s advantages and mitigate the differences between different modes. In this article, we propose a high-low-frequency progressive-guided diffusion model. It is used to generate an image with the advantages of both MS and PAN, which can be complementary to MS and PAN and, thus, can better reduce the modal differences between them. Therefore, we use the fusion image as an auxiliary mode and an intermediate bridge, which can better connect the characteristics between various sources. First, we design guidance information that contains the advantages of MS and PAN, and some operations can make this information better guide the generation stage. In addition, we design a high-low-frequency progressive guidance strategy; by using this strategy, we can first ensure the overall structure and layout of the image in the generation stage and then refine the local details and features of the image. This dramatically improves the quality of the generated image. Finally, we use mathematical knowledge to explain the rationality of the strategy. We validate our method on multiple datasets and achieve the best performance. Our code is https://github.com/Xidian-AIGroup190726/HLF-GDiffusion.

I. Introduction

Remote sensing images are surface information obtained through platforms, such as satellites or aircraft, which have a wide range of applications in agriculture, the environment, urban planning, disaster monitoring, and so on. With the advancement of technology, many remote sensing satellites, such as IKONOS, QuickBird, Deimos-2, and Gaofen-2, can simultaneously capture panchromatic (PAN) images and multispectral (MS) images. However, due to the limitations of sensor technology, the spectral information of MS is rich, but spatial information is insufficient, and the spatial information of PAN graph is rich, but spectral information is insufficient, indicating that the two have good complementarity.