高分辨率遥感影像具有更丰富多彩的纹理特征及邻接关系等几何图像数据信息，能为遥感影像智能解译提供可靠的信息来源，也加深了其在变化检测（Change Detection，CD）中的应用，现有的CD算法面临巨大挑战：一是网络依靠逐层卷积实现特征提取，缺少对重要信息的挖掘，上采样过程缺乏有效连接，导致网络学习到的变化特征有限；二是网络受噪声干扰，在获取图像特征的同时难以充分挖掘图像间的差异信息；三是训练过程中难易样本分类问题会影响网络精度。因此，本文在深入分析当前高分辨率遥感影像变化检测技术的基础上，结合深度学习技术对CD任务展开研究，具体研究内容如下：
（1）通过在分割网络UNet+++中添加坐标注意力机制（Coordination Attention,CA），充分学习来自编码器的全尺度语义并解决图像特征提取过程中信息获取不足的问题。对比自注意力、通道注意力、卷积注意力集成结果，CA UNet+++在LEBEDV和LEVIR-CD两种不同变化类型的数据集上结果最优，综合评价指标（F1-Score,F1）和总体精度（Overall Accuracy,OA）分别为89.2%、97.4%和87.2%、97.0%。通过分析不同注意力模块对网络中间层的特征响应，证明注意力机制是增强网络特征提取能力的潜在手段。
（2）针对早期融合操作带来的原始图像高维特征和图像对间差异性息难以获取等问题，提出基于孪生结构和全尺度特征重构的变化检测网络Siamese UNet+++，利用权值共享的孪生结构编码器提取两期影像不同尺度的特征信息及其差异信息，在解码阶段，连接差异信息和后一时相的高维特征进行全尺度特征重构，减少特征信息在层级间的消耗。结果表明，Siamese UNet+++在LEBEDV和LEVIR-CD数据集上F1和OA值分别为96.9%、99.2%和90.3%、98.3%，同其他对比方法相比，精度有所提高。不同全尺度连接方式对网络精度产生一定影响，本文设计的方法在上述数据集上最有效。Focal dice loss使网络聚焦于分类困难样本的训练，从而使网络能够察觉出图像对上的细小变化。相较于Dice loss和平衡二元交叉熵组合的混合损失，Focal dice loss对Siamese UNet+++的训练更有效，F1提高了3.5%。
（3）利用Labelme图象注释工具对红沙泉矿区2016和2020年两景Sentinel2影像进行标注，圈出变化区域并分割利用荒漠化矿区数据集对本文方法进行验证分析。结果表明，CA UNet+++和Siamese UNet+++方法在该数据集上具有一定适用性，同基准方法相比，精度达到最优和次优，F1和OA分别为80.1%、99.5%和79.2%、99.5%，同时，上述方法受噪声干扰程度低于其他基准方法，能够得到完整紧凑的矿区变化区域。

High-resolution remote sensing images have more colorful geometric image data information such as texture features and adjacency relationships, which can provide a reliable source of information for intelligent interpretation of remote sensing images, and deepen their application in Change Detection (CD). Existing CD algorithms face huge challenges: First, the network relies on layer-by-layer convolution for feature extraction, lacks the mining of important changing features, and the upsampling process lacks effective connections, resulting in limited changing features learned by the network; second, the network is affected by Due to noise interference, it is difficult to fully exploit the difference information between images while obtaining image features; third, the problem of difficult and easy sample classification in the training process will affect the network accuracy. Therefore, this paper deeply analyzes the basis of the current high-resolution remote sensing image change detection technology In the above, combined with deep learning technology, the CD task is studied. The specific research contents are as follows:

(1)   By adding the Coordination Attention (CA) mechanism to the segmentation network UNet+++, the full-scale semantics from the encoder is fully learned and the problem of insufficient information acquisition in the image feature extraction process is solved. Comparing the results of self-attention, channel attention, and convolutional attention integration, CA UNet+++ has the best results on two datasets with different variation types, LEBEDV and LEVIR-CD, with F1 and OA of 89.2%, 97.4% and 87.2%, 97.0%, respectively. by analyzing the feature responses of different attention modules to the middle layer of the network, it is demonstrated that the attention mechanism is a potential means to enhance the network potential means of feature extraction capability.

(2) To address the problems brought about by the early fusion operation, such as the difficulty in obtaining the high-dimensional features of the original image and the disparity information between image pairs, a change detection network Siamese UNet+++ based on twin structure and full-scale feature reconstruction is proposed to extract the feature information and its disparity information at different scales of the two image phases using the twin structure encoder with shared weights, and in the decoding phase, the disparity information is connected with the high-dimensional information of the later phase In the decoding stage, the full-scale feature reconstruction is performed to reduce the consumption of feature information between layers. The results show that the F1 and OA values of Siamese UNet+++ are 96.9%, 99.2% and 90.3%, 98.3% on LEBEDV and LEVIR-CD datasets, respectively, and the accuracy is improved compared with other comparison methods. Focal dice loss allows the network to focus on the training of difficult samples for classification, thus enabling the network to detect small changes in the image pairs. Focal dice loss is more effective for training Siamese UNet+++ with a 3.5% improvement in F1 compared to a mixture of Dice loss and balanced binary cross-entropy combination.

(3)   The Labelme image annotation tool was used to annotate two scenes of Sentinel2 images of the Red Sand Spring mining area in 2016 and 2020, circle the change area and segmentation using the desertification mining area dataset for validation analysis of the method in this paper. The results show that the CA UNet+++ and Siamese UNet+++ methods have some applicability on this dataset, and the accuracy reaches the optimal and suboptimal compared with the benchmark methods, with F1 and OA of 80.1%, 99.5% and 79.2%, 99.5%, respectively, while the above methods are less disturbed by noise than other benchmark methods and can obtain complete and compact change areas of the mine area.

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