遥感影像变化检测在土地利用与覆盖变化监测、城市发展变化研究、环境监测和灾害评估等领域具有很强的理论意义和应用价值，一直是国内外学者关注的热点领域。然而，随着影像空间分辨率的提高，高分辨率遥感影像呈现出大量的新特点，如空间特征丰富、地物目标多尺度化等，而传统的遥感影像变化检测方法受限于人为设计特征的表达能力，在高分辨率遥感影像变化检测上的精度仍有待提高。
近年来，由于在深度特征提取能力上的优势，以卷积神经网络为代表的深度学习技术在图像处理领域展现出了巨大的潜力。但是，遥感影像变化检测任务具有其特殊性，其关注的是同一地区不同时相影像间的变化信息，所以需要建模影像间的时空相关性以突出变化信息。因此，本文首先在建模影像间时空相关性的基础上，从基于像素和基于对象的角度，开展了基于语义分割模型和基于实例分割模型的高分辨率遥感影像变化检测方法研究，并进一步探讨基于卷积神经网络的端到端变化检测模型设计，以期在高分辨率遥感影像变化检测任务中实现较高的精度和较好的边缘保持效果。本文的主要工作和结论总结如下：
(1) 提出特征相关性网络组件，以构建影像间的时空相关性。本文通过在典型的语义分割模U-Net和实例分割模型Mask R-CNN上添加一个能够建模时空相关性的简单组件，并结合孪生网络，使这些模型能够适用于解决遥感影像变化检测问题。进一步地，在总结U-Net和Mask R-CNN的特点之后，提出特征相关性网络，其包含相关性计算和多层次特征融合两个步骤，旨在合理地构建影像间的时空相关性。最后讨论了相关性计算的多种实现方式，实验表明，一种简单的无参数相关性计算方法就可以实现较好的效果。
(2) 不同语义分割模型下的高分辨率遥感影像变化检测研究，旨在从基于像素的角度理解语义分割背景下的高分辨率遥感影像变化检测任务。在具体实现上，根据特征相关性网络的思路，将三个经典的语义分割模型 (SemanticFPN、DeepLabv3和DeepLabv3+) 修改为变化检测模型，并进行相关实验，根据实验结果进行了详细的定量和定性分析，最后对比分析了不同的基于语义分割模型 (U-Net、SemanticFPN、DeepLabv3和DeepLabv3+) 的变化检测模型的优缺点。根据实验结果和对比分析，对于基于语义分割模型的变化检测模型的网络架构设计和性能，可以得出以下重要的结论：1) 多层次特征融合能够提升模型的性能，并且利用的特征提取器中的层次特征越多，模型的性能和泛化能力越好，这也证明了特征相关性网络中的多层次特征融合的必要性；2) 基于语义分割模型的变化检测模型对于边缘的预测效果离理想结果还有一定距离，尤其是对于密集的变化区域，这些模型很容易将多个变化区域预测成一个变化区域，这说明模型的边缘预测能力还有待提升。
(3) 不同实例分割模型下的高分辨率遥感影像变化检测研究，旨在从基于对象的角度理解实例分割背景下的高分辨率遥感影像变化检测任务。在具体实现上，根据特征相关性网络的思路，将两个典型的实例分割模型 (BlendMask和CenterMask) 修改为变化检测模型，并进行相关实验，然后根据实验结果进行了详细的定量和定性分析，最后对比分析了基于实例分割模型 (Mask R-CNN、BlendMask和CenterMask) 的变化检测模型和基于语义分割模型 (U-Net、SemanticFPN、DeepLabv3和DeepLabv3+) 的变化检测模型的优缺点。根据实验结果和对比分析，可以得出基于实例分割模型的变化检测模型和基于语义分割模型的变化检测模型的主要区别是：基于实例分割模型的变化检测模型是对图片的子区域进行分割，而基于语义分割模型的变化检测模型是对图片的整个区域进行分割，所以，基于实例分割模型的变化检测模型相对于基于语义分割模型的变化检测模型而言，更擅长于将密集变化区域的边界分开，却容易将一个大型的变化区域分割成多个小型的变化区域，而基于语义分割模型的变化检测模型正好相反。
(4) 针对基于卷积神经网络的变化检测模型对于变化边缘预测不够准确的问题，提出边缘感知网络。边缘感知网络由主干网、粗预测分支和精预测分支三部分组成，主干网用于多层次相关性特征的提取；粗预测分支用于多层次特征图的融合，以建模变化区域内部语义信息的一致性；精预测分支用于多层次特征点的融合，以尽可能保留边缘细节信息。在航空影像和卫星影像的建筑物变化检测数据集上的实验表明，相较于其它方法，边缘感知网络既能够取得最优的变化检测精度，也能够有效地保留边界的细节信息。

Remote sensing image change detection has strong theoretical significance and application value in the fields of land use and cover change monitoring, urban development and change research, environmental monitoring and disaster assessment, etc., and has always been a hot field of concern for scholars at home and abroad. However, with the improvement of image spatial resolution, high resolution remote sensing images show a lot of new characteristics, such as rich spatial features and object multiscaling, etc., and the traditional remote sensing image change detection method is limited by artificial design characteristics of the power of expression, on the high-resolution remote sensing image change detection accuracy remains to be improved.
In recent years, due to its advantages in deep feature extraction, deep learning technology represented by the convolutional neural networks has shown great potential in the field of image processing. However, the task of remote sensing image change detection has its particularity, which focuses on the change information between images of different times in the same region, so the spatio-temporal correlation between images needs to be modeled to highlight the change information. Therefore, this thesis based on the modeling spatio-temporal correlation between images, from the perspective of based on pixel and based on the object, carried out research based on semantic segmentation models and instance segmentation models of high-resolution remote sensing image change detection method, and further explore the end-to-end change detection network architecture design based on convolution neural network, to achieve higher accuracy and better edge result in high-resolution remote sensing image change detection task. The main work and conclusions of this thesis are summarized as follows:
(1) A feature correlation network is proposed to construct the spatio-temporal correlation between images. In this section, a simple component that can model the temporal and spatial correlation is added to the existing typical semantic segmentation model (U-Net) and the instance segmentation model (Mask R-CNN), and combined with the siamese network, so that these models can be applied to solve the problem of remote sensing image change detection. After summarizing the characteristics of U-Net and Mask R-CNN, the Feature Correlation Network (FCoN) is proposed. The FCoN includes two steps of correlation calculation and multi-level feature fusion, which aims to effectively construct the spatio-temporal correlation between images. In the end, we discuss several ways of correlation calculation, and the experiment shows that a simple parameterless correlation calculation method already achieves excellent results.
(2) The study on high-resolution remote sensing image change detection under different semantic segmentation models aims to understand the task of high-resolution remote sensing image change detection under semantic segmentation background from the perspective of pixel-based. Specifically, according to the design of FCoN, the three classical semantic segmentation models (SemanticFPN, DeepLabv3, and DeepLabv3+) are modified to change detection models, and relevant experiments were carried out. According to the experimental results, a detailed quantitative and qualitative analysis was conducted, and the advantages and disadvantages of these models are also analyzed. According to the experimental results and comparative analysis, for change detection model’s network architecture design and performance based on semantic segmentation, we can draw the following conclusions: 1) Multi-level feature fusion can improve the performance of the model, and the more hierarchical features in the feature extractor, the better the performance and generalization ability of the model, which also proves the necessity of multi-level feature fusion in the FCoN; 2) The prediction of the selected typical semantic segmentation model on the edge is still far from the ideal result. Especially for the dense change area, these models are easy to predict the change area of multiple areas into a area, which indicates that the edge prediction ability of the model needs to be improved.
(3) The study on high-resolution remote sensing image change detection under different instance segmentation models aims to understand the task of high-resolution remote sensing image change detection under the background of instance segmentation from the object-based perspective. Specifically, according to the idea of FCoN, the two typical instance segmentation models (BlendMask and CenterMask) are modified into change detection models, and relevant experiments were carried out. According to the experimental results, a detailed quantitative and qualitative analysis was conducted. Finally, the advantages and disadvantages of the change detection model based on instance segmentation model (Mask R-CNN, BlendMask, and CenterMask) and the change detection model based on semantic segmentation models (U-Net, SemanticFPN, DeepLabv3, and DeepLabv3+) were compared and analyzed. According to the experimental results and comparative analysis, the main differences between the change detection method based on the instance segmentation and the change detection model based on semantic segmentation are as follows: The change detection models based on instance segmentation are to segment the sub-region of the image, while the change detection model based on semantic segmentation are to segment the entire region of the image, therefore, the change detection models based on instance segmentation, are better at separating the dense change areas, but is easier to divide a large area into multiple areas, and change detection models based on semantic segmentation are just the opposite.
(4) The Edge Sensing Network (ESNet) is proposed to obtain better change detection accuracy and effectively retain the detailed information of the edge. ESNet is composed of a backbone, coarse prediction branch, and fine prediction branch. The backbone is used to extract multi-level correlation features. The coarse prediction branch is used for the fusion of multi-level feature maps to model the consistency of change object's inner. The fine prediction branch is used for the fusion of multi-level feature points to retain edge details as much as possible. Compared with other methods, the ESNet can not only obtain the optimal change detection accuracy but also effectively retain the detailed information of the edge.

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