Bitemporal supervised learning paradigm always dominates remote sensing
change detection using numerous labeled bitemporal image pairs, especially for
high spatial resolution (HSR) remote sensing imagery. However, it is very
expensive and labor-intensive to label change regions in large-scale bitemporal
HSR remote sensing image pairs. In this paper, we propose single-temporal
supervised learning (STAR) for universal remote sensing change detection from a
new perspective of exploiting changes between unpaired images as supervisory
signals. STAR enables us to train a high-accuracy change detector only using
unpaired labeled images and can generalize to real-world bitemporal image
pairs. To demonstrate the flexibility and scalability of STAR, we design a
simple yet unified change detector, termed ChangeStar2, capable of addressing
binary change detection, object change detection, and semantic change detection
in one architecture. ChangeStar2 achieves state-of-the-art performances on
eight public remote sensing change detection datasets, covering above two
supervised settings, multiple change types, multiple scenarios. The code is
available at this https URL

提出了一种新的单时序监督学习方法（STAR），用于遥感变化检测，通过利用未配对图像之间的变化来训练高准确性的变化检测器，并且适用于实际的双时序图像对。通过设计具有二进制变化检测、目标变化检测和语义变化检测能力的简单统一的 ChangeStar2 算法，STAR 的灵活性和可伸缩性得到了证明，该算法在八个公共遥感变化检测数据集上取得了最先进的性能。

通用遥感变化检测的单时序监督学习

Single-Temporal Supervised Learning for Universal Remote Sensing Change  Detection

Low-light image enhancement (LLE) remains challenging due to the unfavorable
prevailing low-contrast and weak-visibility problems of single RGB images. In
this paper, we respond to the intriguing learning-related question -- if
leveraging both accessible unpaired over/underexposed images and high-level
semantic guidance, can improve the performance of cutting-edge LLE models?
Here, we propose an effective semantically contrastive learning paradigm for
LLE (namely SCL-LLE). Beyond the existing LLE wisdom, it casts the image
enhancement task as multi-task joint learning, where LLE is converted into
three constraints of contrastive learning, semantic brightness consistency, and
feature preservation for simultaneously ensuring the exposure, texture, and
color consistency. SCL-LLE allows the LLE model to learn from unpaired
positives (normal-light)/negatives (over/underexposed), and enables it to
interact with the scene semantics to regularize the image enhancement network,
yet the interaction of high-level semantic knowledge and the low-level signal
prior is seldom investigated in previous methods. Training on readily available
open data, extensive experiments demonstrate that our method surpasses the
state-of-the-arts LLE models over six independent cross-scenes datasets.
Moreover, SCL-LLE's potential to benefit the downstream semantic segmentation
under extremely dark conditions is discussed. Source Code:
this https URL

本文提出了一种有效的基于对比学习的语义低光图像增强算法，利用多任务联合学习将低光图像增强任务转化为对比学习、语义亮度一致性和特征保护三个约束，用于同时确保曝光、纹理和颜色的一致性。实验结果表明，该方法优于当前六个独立交叉场景数据集中最好的低光图像增强模型。