以动治静：基于粗粒度时间前景挖掘的遥感变化检测
Treat Stillness with Movement: Remote Sensing Change Detection via Coarse-grained Temporal Foregrounds Mining
AI总结：
针对现有遥感变化检测方法忽略运动线索的问题，提出CTMA框架，通过插值生成视频提取运动特征，结合多模块增强策略，在多个基准数据集上验证了有效性。
摘要：
当前研究主要利用双时相图像解决遥感变化检测任务，尽管能取得不错的性能，但很少有研究考虑可能同样至关重要的运动线索。本文重新审视了被广泛采用的基于双时相图像的框架，提出了一种新颖的粗粒度时间挖掘增强（CTMA）框架。具体而言，给定双时相图像，我们首先通过插值操作将其转换为视频；随后采用一组时间编码器从生成的视频中提取运动特征，用于粗粒度变化区域预测。接着，我们设计了新颖的粗粒度前景增强空间编码器模块，以整合全局与局部信息；还引入了运动增强策略，将运动线索作为额外输出与空间特征聚合，以提升检测结果。同时，我们将输入图像对输入ResNet，获取差异特征以及用于细粒度特征学习的空间块。更重要的是，我们提出了掩码增强策略，利用粗粒度变化区域并将其融入解码器块，以增强最终的变化预测。在多个基准数据集上开展的大量实验充分验证了本文所提框架在遥感图像变化检测任务中的有效性。本文的源代码将发布于https://github.com/Event-AHU/CTM_Remote_Sensing_Change_Detection。
英文摘要：
Current works focus on addressing the remote sensing change detection task using bi-temporal images. Although good performance can be achieved, however, seldom of they consider the motion cues which may also be vital. In this work, we revisit the widely adopted bi-temporal images-based framework and propose a novel Coarse-grained Temporal Mining Augmented (CTMA) framework. To be specific, given the bi-temporal images, we first transform them into a video using interpolation operations. Then, a set of temporal encoders is adopted to extract the motion features from the obtained video for coarse-grained changed region prediction. Subsequently, we design a novel Coarse-grained Foregrounds Augmented Spatial Encoder module to integrate both global and local information. We also introduce a motion augmented strategy that leverages motion cues as an additional output to aggregate with the spatial features for improved results. Meanwhile, we feed the input image pairs into the ResNet to get the different features and also the spatial blocks for fine-grained feature learning. More importantly, we propose a mask augmented strategy that utilizes coarse-grained changed regions, incorporating them into the decoder blocks to enhance the final changed prediction. Extensive experiments conducted on multiple benchmark datasets fully validated the effectiveness of our proposed framework for remote sensing image change detection. The source code of this paper will be released on https://github.com/Event-AHU/CTM_Remote_Sensing_Change_Detection
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
