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

Pyramidal feature representation is the common practice to address the
challenge of scale variation in object detection. However, the inconsistency
across different feature scales is a primary limitation for the single-shot
detectors based on feature pyramid. In this work, we propose a novel and data
driven strategy for pyramidal feature fusion, referred to as adaptively spatial
feature fusion (ASFF). It learns the way to spatially filter conflictive
information to suppress the inconsistency, thus improving the scale-invariance
of features, and introduces nearly free inference overhead. With the ASFF
strategy and a solid baseline of YOLOv3, we achieve the best speed-accuracy
trade-off on the MS COCO dataset, reporting 38.1% AP at 60 FPS, 42.4% AP at 45
FPS and 43.9% AP at 29 FPS. The code is available at
this https URL

在目标检测中，为了应对尺度变化的挑战，金字塔特征表示是一种常见的做法。本文提出了一种新颖且数据驱动的策略 - 自适应空间特征融合（ASFF），通过学习如何空间过滤冲突信息来抑制不一致性，从而提高特征的尺度不变性。通过该策略与 YOLOv3 的结合，在 MS COCO 数据集上取得了最佳的速度 - 精度平衡。