论文标题
pansharpening卷积神经网络的带间和内部损失
An Inter- and Intra-Band Loss for Pansharpening Convolutional Neural Networks
论文作者
论文摘要
Pansharpening的目的是将卫星的全光谱图像和多光谱图像融合在一起，以生成具有高空间和光谱分辨率的图像。随着深度学习在计算机视觉领域的成功应用，许多学者提出了许多卷积神经网络（CNN）来解决pansharpensing任务。这些Pansharpening网络着重于CNN的各种独特结构，其中大多数是通过融合图像和模拟所需的多光谱图像之间的L2损失训练的。但是，L2损失旨在直接最大程度地减少每个频段的光谱信息的差异，这不考虑训练过程中的带间关系。在这封信中，我们提出了一种新颖的频带和内部（IIB）损失，以克服原始L2损失的缺点。我们提出的IIB损失可以有效地保留波带间关系，并可以直接应用于不同的Pansharpening CNN。
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.
