Feature matching determines the orientation accuracy for the High Spatial
Resolution (HSR) optical satellite stereos, subsequently impacting several
significant applications such as 3D reconstruction and change detection.
However, the matching of off-track HSR optical satellite stereos often
encounters challenging conditions including wide-baseline observation,
significant radiometric differences, multi-temporal changes, varying spatial
resolutions, inconsistent spectral resolution, and diverse sensors. In this
study, we evaluate various advanced feature matching algorithms for HSR optical
satellite stereos. Utilizing a specially constructed dataset from five
satellites across six challenging scenarios, HSROSS Dataset, we conduct a
comparative analysis of four algorithms: the traditional SIFT, and
deep-learning based methods including SuperPoint + SuperGlue, SuperPoint +
LightGlue, and LoFTR. Our findings highlight overall superior performance of
SuperPoint + LightGlue in balancing robustness, accuracy, distribution, and
efficiency, showcasing its potential in complex HSR optical satellite
scenarios.

通过对高空间分辨率 (HSR) 光学卫星立体图像的特征匹配算法进行评估，本研究发现 SuperPoint + LightGlue 算法在平衡稳健性、准确性、分布性和效率方面表现出优秀的综合性能，显示了其在复杂 HSR 光学卫星场景中的潜力。

具有挑战性的高空间分辨率光学卫星立体场景中先进特征匹配算法的比较分析

Comparative Analysis of Advanced Feature Matching Algorithms in  Challenging High Spatial Resolution Optical Satellite Stereo Scenarios

For high spatial resolution (HSR) remote sensing images, bitemporal
supervised learning always dominates change detection using many pairwise
labeled bitemporal images. However, it is very expensive and time-consuming to
pairwise label large-scale bitemporal HSR remote sensing images. In this paper,
we propose single-temporal supervised learning (STAR) for change detection from
a new perspective of exploiting object changes in unpaired images as
supervisory signals. STAR enables us to train a high-accuracy change detector
only using \textbf{unpaired} labeled images and generalize to real-world
bitemporal images. To evaluate the effectiveness of STAR, we design a simple
yet effective change detector called ChangeStar, which can reuse any deep
semantic segmentation architecture by the ChangeMixin module. The comprehensive
experimental results show that ChangeStar outperforms the baseline with a large
margin under single-temporal supervision and achieves superior performance
under bitemporal supervision. Code is available at
this https URL

提出了一种利用非配对图像中的对象变化作为监督信号的单时相监督学习（STAR）方法，可以利用未配对标记图像训练高精度变化检测器，并且可以推广到现实世界的双时相图像。综合实验结果表明，该方法在单时相和双时相的监督下均表现出优异性能。

遍地变革：遥感图像单时序监督目标变化检测

Change is Everywhere: Single-Temporal Supervised Object Change Detection  in Remote Sensing Imagery

Magnetic resonance image (MRI) in high spatial resolution provides detailed
anatomical information and is often necessary for accurate quantitative
analysis. However, high spatial resolution typically comes at the expense of
longer scan time, less spatial coverage, and lower signal to noise ratio (SNR).
Single Image Super-Resolution (SISR), a technique aimed to restore
high-resolution (HR) details from one single low-resolution (LR) input image,
has been improved dramatically by recent breakthroughs in deep learning. In
this paper, we introduce a new neural network architecture, 3D Densely
Connected Super-Resolution Networks (DCSRN) to restore HR features of
structural brain MR images. Through experiments on a dataset with 1,113
subjects, we demonstrate that our network outperforms bicubic interpolation as
well as other deep learning methods in restoring 4x resolution-reduced images.

本文提出了一种新的神经网络体系结构（3D Densely Connected Super-Resolution Networks），用于从单张低分辨率 MRI 图像中恢复高分辨率的脑部结构特征，实验表明该网络在还原 4 倍分辨率缩减图像方面优于双三次插值和其他深度学习方法。