This research presents a comprehensive assessment of pan-sharpening
techniques for satellite imagery, focusing on the critical aspects of spectral
fidelity and spatial enhancement. Motivated by the need for informed algorithm
selection in remote sensing, A novel cascaded and structured evaluation
framework has been proposed with a detailed comparative analysis of existing
methodologies. The research findings underscore the intricate trade-offs
between spectral accuracy of about 88\% with spatial resolution enhancement.
The research sheds light on the practical implications of pan-sharpening and
emphasizes the significance of both spectral and spatial aspects in remote
sensing applications. Various pan-sharpening algorithms were systematically
employed to provide a holistic view of their performance, contributing to a
deeper understanding of their capabilities and limitations.

该研究提供了卫星影像的全面融合技术评估，重点关注光谱保真度和空间增强两个关键方面。通过建立一种新颖的级联结构评估框架，对现有方法进行了详细比较分析，结果显示在光谱精度提高 88% 的同时实现了空间分辨率的增强。研究揭示了融合技术在遥感应用中的实际意义，并强调了光谱和空间两个方面的重要性。通过系统地采用多种融合算法，对它们的性能进行了整体观察，有助于更深入地了解它们的能力和局限。

卫星图像的光谱保真度和空间增强评估及级联图像融合技术

Spectral Fidelity and Spatial Enhancement: An Assessment and Cascading  of Pan-Sharpening Techniques for Satellite Imagery

In this paper, we propose a novel approach to tackle the multiple instance
regression (MIR) problem. This problem arises when the data is a collection of
bags, where each bag is made of multiple instances corresponding to the same
unique real-valued label. Our goal is to train a regression model which maps
the instances of an unseen bag to its unique label. This MIR setting is common
to remote sensing applications where there is high variability in the
measurements and low geographical variability in the quantity being estimated.
Our approach, in contrast to most competing methods, does not make the
assumption that there exists a prime instance responsible for the label in each
bag. Instead, we treat each bag as a set (i.e, an unordered sequence) of
instances and learn to map each bag to its unique label by using all the
instances in each bag. This is done by implementing an order-invariant
operation characterized by a particular type of attention mechanism. This
method is very flexible as it does not require domain knowledge nor does it
make any assumptions about the distribution of the instances within each bag.
We test our algorithm on five real world datasets and outperform previous
state-of-the-art on three of the datasets. In addition, we augment our feature
space by adding the moments of each feature for each bag, as extra features,
and show that while the first moments lead to higher accuracy, there is a
diminishing return.

本文提出了一种新的方法来处理多实例回归问题，使用基于注意力机制的无序操作来训练模型，并测试证明在真实数据集上优于以往方法，同时探讨特征空间维度数量对结果的影响。