An Inter- and Intra-Band Loss for Pansharpening Convolutional Neural Networks

TL;DR

This paper introduces a novel inter- and intra-band loss function for pansharpening CNNs, improving spectral relation preservation over traditional L2 loss, and can be integrated into various models.

Contribution

The paper proposes the IIB loss, a new training loss that better preserves spectral and inter-band relations in pansharpening CNNs.

Findings

IIB loss enhances spectral fidelity in fused images.

The method is compatible with different CNN architectures.

Experimental results show improved spectral and spatial quality.

Abstract

Pansharpening aims to fuse panchromatic and multispectral images from the satellite to generate images with both high spatial and spectral resolution. With the successful applications of deep learning in the computer vision field, a lot of scholars have proposed many convolutional neural networks (CNNs) to solve the pansharpening task. These pansharpening networks focused on various distinctive structures of CNNs, and most of them are trained by L2 loss between fused images and simulated desired multispectral images. However, L2 loss is designed to directly minimize the difference of spectral information of each band, which does not consider the inter-band relations in the training process. In this letter, we propose a novel inter- and intra-band (IIB) loss to overcome the drawback of original L2 loss. Our proposed IIB loss can effectively preserve both inter- and intra-band relations and can be directly applied to different pansharpening CNNs.