基于随机补片和DeepLabV3+的建筑物遥感图像变化检测

为有效解决传统遥感图像变化检测预处理复杂的问题，提出一种基于随机补片和DeepLabV3+的建筑物遥感图像变化检测方法。以ResNet50特征提取网络为基础，创建DeepLabV3+语义分割网络，并在图像和标签中创建大小为224像素×224像素的随机补片作为网络输入，训练建筑物提取网络；修改建筑物提取网络输入层为6通道，通过矩阵运算将两期遥感图像转换为一幅6通道非RGB图像，利用转换后的非RGB图像进行网络训练并验证变化检测精度。实验1利用ENVI5.3软件，采用马氏距离法进行变化检测；实验2采用改进的U-Net网络和随机补片，完成网络训练和精度验证；实验3使用实验2的训练数据和验证数据，采用随机补片和DeepLabV3+网络进行变化检测网络训练及精度验证。实验结果表明，该方法实验1、实验2、实验3建筑物变化检测平均交并比分别为24.43%、83.14%、89.90%，边界轮廓匹配分数分别为61.47%，80.24%、96.51%。

Remote Sensing Image Change Detection Based on Random Patches and DeepLabV3+ Network

In order to effectively preprocess the traditional remote sensing image change detection, we proposed a change detection method of building remote sensing image based on random patches and DeeplabV3+. This method builds a DeepLabV3+ semantic segmentation network based on the ResNet50, which is a feature extraction network, crops the random patches of 224 pixels×224 pixels in the image and label them as the network input to train the building extraction network,and then, modify the input layer of the building extraction network to six channels. The two-phase remote sensing images are converted into a 6-channel non-RGB image through matrix operation, which are used for network training and validating the change detection accuracy. In Experiment 1, the Mahalanobis distance classification method was used to detect the change by ENVI5.3 software. In Experiment 2, the improved U-Net network and random patches were used to complete the network training and accuracy verification. Experiment 3 used the training data and verification data of Experiment 2, and used random patches and DeepLabV3+ network to train the change detection network and verify the accuracy.The results of Experiment 1, 2, and 3 show that the average intersection-over-union of this method is 24.43%, 83.14%, and 89.90% respectively, and the boundary matching score is 61.47%, 80.24%, and 96.51% respectively.