Remote sensing images captured by different platforms exhibit significant
disparities in spatial resolution. Large scale factor super-resolution (SR)
algorithms are vital for maximizing the utilization of low-resolution (LR)
satellite data captured from orbit. However, existing methods confront
challenges in recovering SR images with clear textures and correct ground
objects. We introduce a novel framework, the Semantic Guided Diffusion Model
(SGDM), designed for large scale factor remote sensing image super-resolution.
The framework exploits a pre-trained generative model as a prior to generate
perceptually plausible SR images. We further enhance the reconstruction by
incorporating vector maps, which carry structural and semantic cues. Moreover,
pixel-level inconsistencies in paired remote sensing images, stemming from
sensor-specific imaging characteristics, may hinder the convergence of the
model and diversity in generated results. To address this problem, we propose
to extract the sensor-specific imaging characteristics and model the
distribution of them, allowing diverse SR images generation based on imaging
characteristics provided by reference images or sampled from the imaging
characteristic probability distributions. To validate and evaluate our
approach, we create the Cross-Modal Super-Resolution Dataset (CMSRD).
Qualitative and quantitative experiments on CMSRD showcase the superiority and
broad applicability of our method. Experimental results on downstream vision
tasks also demonstrate the utilitarian of the generated SR images. The dataset
and code will be publicly available at this https URL

通过利用预训练的生成模型作为先验，结合结构和语义线索，以及提取感知一致的传感器特性并模拟其分布，从而提高遥感影像的超分辨率。实验证明了该方法的卓越性能和广泛适用性。

基于语义指导的大规模因子化遥感图像超分辨率生成扩散先验

Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution  with Generative Diffusion Prior

Vector maps are essential in autonomous driving for tasks like localization
and planning, yet their creation and maintenance are notably costly. While
recent advances in online vector map generation for autonomous vehicles are
promising, current models lack adaptability to different sensor configurations.
They tend to overfit to specific sensor poses, leading to decreased performance
and higher retraining costs. This limitation hampers their practical use in
real-world applications. In response to this challenge, we propose a modular
pipeline for vector map generation with improved generalization to sensor
configurations. The pipeline leverages probabilistic semantic mapping to
generate a bird's-eye-view (BEV) semantic map as an intermediate
representation. This intermediate representation is then converted to a vector
map using the MapTRv2 decoder. By adopting a BEV semantic map robust to
different sensor configurations, our proposed approach significantly improves
the generalization performance. We evaluate the model on datasets with sensor
configurations not used during training. Our evaluation sets includes larger
public datasets, and smaller scale private data collected on our platform. Our
model generalizes significantly better than the state-of-the-art methods.

我们提出了一种用于自动驾驶中向量地图生成的模块化流水线，通过采用鲁棒的俯视语义地图，并利用 MapTRv2 解码器将其转换为向量地图，从而显著提高了模型的泛化性能。在未经训练的传感器配置数据集上进行的评估结果表明，我们的模型泛化能力明显优于最先进的方法。