As an important task in remote sensing image analysis, remote sensing change
detection (RSCD) aims to identify changes of interest in a region from
spatially co-registered multi-temporal remote sensing images, so as to monitor
the local development. Existing RSCD methods usually formulate RSCD as a binary
classification task, representing changes of interest by merely feature
concatenation or feature subtraction and recovering the spatial details via
densely connected change representations, whose performances need further
improvement. In this paper, we propose STNet, a RSCD network based on spatial
and temporal feature fusions. Specifically, we design a temporal feature fusion
(TFF) module to combine bi-temporal features using a cross-temporal gating
mechanism for emphasizing changes of interest; a spatial feature fusion module
is deployed to capture fine-grained information using a cross-scale attention
mechanism for recovering the spatial details of change representations.
Experimental results on three benchmark datasets for RSCD demonstrate that the
proposed method achieves the state-of-the-art performance. Code is available at
this https URL

本篇论文提出了一种基于时空特征融合的 RSCD 网络 STNet，通过时间特征融合模块和空间特征融合模块强化感兴趣的变化并恢复变化表征的空间细节，实验表明该方法在三个基准数据集上取得了最先进的性能。

STNet：用于遥感图像变化检测的时空特征融合网络

STNet: Spatial and Temporal feature fusion network for change detection  in remote sensing images

Despite the success of deep learning for static image understanding, it
remains unclear what are the most effective network architectures for the
spatial-temporal modeling in videos. In this paper, in contrast to the existing
CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial
temporal network (StNet) architecture for both local and global
spatial-temporal modeling in videos. Particularly, StNet stacks N successive
video frames into a \emph{super-image} which has 3N channels and applies 2D
convolution on super-images to capture local spatial-temporal relationship. To
model global spatial-temporal relationship, we apply temporal convolution on
the local spatial-temporal feature maps. Specifically, a novel temporal
Xception block is proposed in StNet. It employs a separate channel-wise and
temporal-wise convolution over the feature sequence of video. Extensive
experiments on the Kinetics dataset demonstrate that our framework outperforms
several state-of-the-art approaches in action recognition and can strike a
satisfying trade-off between recognition accuracy and model complexity. We
further demonstrate the generalization performance of the leaned video
representations on the UCF101 dataset.

本文提出了一种新的空时网络（StNet）架构来进行局部和全局的空时建模，通过将 N 个连续的视频帧堆叠成一个超级图像，并对超级图像应用二维卷积来捕获局部空时关系，再对局部空时特征映射应用时间卷积来建模全局空时关系，该方法在动作识别方面优于现有技术，也在模型复杂度和准确度之间取得了理想的平衡，实验结果表明该方法可以广泛应用于视频表示的学习。