The purpose of remote sensing image change detection (RSCD) is to detect
differences between bi-temporal images taken at the same place. Deep learning
has been extensively used to RSCD tasks, yielding significant results in terms
of result recognition. However, due to the shooting angle of the satellite, the
impacts of thin clouds, and certain lighting conditions, the problem of fuzzy
edges in the change region in some remote sensing photographs cannot be
properly handled using current RSCD algorithms. To solve this issue, we
proposed a Body Decouple Multi-Scale by fearure Aggregation change detection
(BD-MSA), a novel model that collects both global and local feature map
information in the channel and space dimensions of the feature map during the
training and prediction phases. This approach allows us to successfully extract
the change region's boundary information while also divorcing the change
region's main body from its boundary. Numerous studies have shown that the
assessment metrics and evaluation effects of the model described in this paper
on the publicly available datasets DSIFN-CD and S2Looking are the best when
compared to other models.

提出了一种新模型 BD-MSA，通过将特征图的全局和局部特征信息集合起来，从而成功地提取变化区域的边界信息并分离变化区域的主体，该模型在公开数据集 DSIFN-CD 和 S2Looking 上的评估效果是最优的。

BD-MSA：基于多尺度特征信息聚合引导的身体解耦 VHR 遥感图像变化检测方法

BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method  guided by multi-scale feature information aggregation

Remote sensing image change detection aims to identify the differences
between images acquired at different times in the same area. It is widely used
in land management, environmental monitoring, disaster assessment and other
fields. Currently, most change detection methods are based on Siamese network
structure or early fusion structure. Siamese structure focuses on extracting
object features at different times but lacks attention to change information,
which leads to false alarms and missed detections. Early fusion (EF) structure
focuses on extracting features after the fusion of images of different phases
but ignores the significance of object features at different times for
detecting change details, making it difficult to accurately discern the edges
of changed objects. To address these issues and obtain more accurate results,
we propose a novel network, Triplet UNet(T-UNet), based on a three-branch
encoder, which is capable to simultaneously extract the object features and the
change features between the pre- and post-time-phase images through triplet
encoder. To effectively interact and fuse the features extracted from the three
branches of triplet encoder, we propose a multi-branch spatial-spectral
cross-attention module (MBSSCA). In the decoder stage, we introduce the channel
attention mechanism (CAM) and spatial attention mechanism (SAM) to fully mine
and integrate detailed textures information at the shallow layer and semantic
localization information at the deep layer.

远程感知图像变化检测中存在虚警和缺失检测的问题。本论文提出了一种新型网络 Triplet UNet (T-UNet)，通过三支编码器同时提取目标特征和前后时间相位图像之间的变化特征。为了有效地交互和融合三支编码器提取的特征，我们提出了一个多支空间 - 光谱交叉注意力模块 (MBSSCA)。在解码器阶段，引入了通道注意力机制 (CAM) 和空间注意力机制 (SAM)，以充分挖掘浅层的细节纹理信息和深层的语义定位信息。

T-UNet：用于高分辨率遥感影像变化检测的三元 UNet

T-UNet: Triplet UNet for Change Detection in High-Resolution Remote  Sensing Images

Popular Transformer networks have been successfully applied to remote sensing
(RS) image change detection (CD) identifications and achieve better results
than most convolutional neural networks (CNNs), but they still suffer from two
main problems. First, the computational complexity of the Transformer grows
quadratically with the increase of image spatial resolution, which is
unfavorable to very high-resolution (VHR) RS images. Second, these popular
Transformer networks tend to ignore the importance of fine-grained features,
which results in poor edge integrity and internal tightness for largely changed
objects and leads to the loss of small changed objects. To address the above
issues, this Letter proposes a Lightweight Structure-aware Transformer (LSAT)
network for RS image CD. The proposed LSAT has two advantages. First, a
Cross-dimension Interactive Self-attention (CISA) module with linear complexity
is designed to replace the vanilla self-attention in visual Transformer, which
effectively reduces the computational complexity while improving the feature
representation ability of the proposed LSAT. Second, a Structure-aware
Enhancement Module (SAEM) is designed to enhance difference features and edge
detail information, which can achieve double enhancement by difference
refinement and detail aggregation so as to obtain fine-grained features of
bi-temporal RS images. Experimental results show that the proposed LSAT
achieves significant improvement in detection accuracy and offers a better
tradeoff between accuracy and computational costs than most state-of-the-art CD
methods for VHR RS images.

为了解决 Transformer 网络在遥感图片变化检测中可能存在的问题，本文提出了一种轻量级结构感知 Transformer（LSAT）网络，有效地提高了 RS 图片 CD 的特征表达能力，并在各种 VHR RS 图片 CD 方法中实现了更好的检测精度和计算成本平衡。