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摘要: 针对目前人为探察土地资源利用情况的任务繁重、办事效率低下等问题, 提出了一种基于深度卷积神经网络的建筑物变化检测方法, 利用高分辨率遥感图像实时检测每个区域新建与扩建的建筑物, 以方便对土地资源进行有效管理.本文受超列(Hypercolumn)和FlowNet中的细化(Refinement)结构启发, 将细化和其他改进应用到U-Net, 提出FlowS-Unet网络.首先对遥感图像裁剪、去噪、标注语义制作数据集, 将该数据集划分为训练集和测试集, 对训练集进行数据增强, 并根据训练集图像的均值和方差对所有图像进行归一化; 然后将训练集输入集成了多尺度交叉训练、多重损失计算、Adam优化的全卷积神经网络FlowS-Unet中进行训练; 最后对网络模型的预测结果进行膨胀、腐蚀以及孔洞填充等后处理得到最终的分割结果.本文以人工分割结果为参考标准进行对比测试, 用FlowS-Unet检测得到的F1分数高达0.943, 明显优于FCN和U-Net的预测结果.实验结果表明, FlowS-Unet能够实时准确地将新建与扩建的建筑物变化检测出来, 并且该模型也可扩展到其他类似的图像检测问题中.
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关键词:
- FlowS-Unet /
- 建筑物变化检测 /
- 全卷积神经网络 /
- 多尺度交叉训练 /
- 多重损失
Abstract: Since manually detecting the situation of land resource utilization is arduous and inefficient, a smart building change detection method based on deep convolutional network is proposed, which can detect newly emerged or expanded buildings in each region of the high-resolution remote sensing images at real-time, thus can be used to manage the land resources efficiently. This article proposes a model named FlowS-Unet by applying refinement and other improvements to U-Net, which was inspired by hypercolumns and the refinement structure in FlowNet. First, the remote sensing images were cropped, denoised, and semantically annotated to form the dataset which is further divided into the training set and testing set, the training set is augmented to get enough training samples, and the mean value and variance of all training images are calculated and used to normalize the dataset; Second, the training set is fed into the fully convolutional network FlowS-Unet for training, which integrates multi-scale cross training, multiple losses and Adam algorithm for its optimization. Finally, the predicted result of FlowS-Unet is further post-processed with dilating, eroding and hole-filling to get the final segmentation result. By using manually segmented results as the ground truth, a comparison with several different algorithms shows that the F1 score of FlowS-Unet is as high as 0.943, which is apparently better than the predicted results of fully convolutional networks (FCN) and U-Net. Experimental results indicate that the newly emerged or expanded buildings can be accurately detected at real time with FlowS-Unet. This model can also be applied to other similar image detection problems.-
Key words:
- FlowS-Unet /
- change detection for buildings /
- fully convolutional networks (FCN) /
- multi-scale cross training /
- multiple losses
1) 本文责任编委 刘青山 -
图 1 一种端到端的建筑物变化检测概览图
Fig. 1 An overview of end-to-end architecture of building change detection
图 4 减少拼接区域色差效果图
Fig. 4 The result after the color difference of stitching blocks was sup-pressed
图 10 FlowS-Unet与现有方法的定性比较
Fig. 10 A quality comparison of FlowS-Unet and previous methods
表 1 FlowS-Unet与现有方法的性能比较
Table 1 The performance comparison of FlowS-Unet
序号 方法 F1分数(后处理前/后) 时间(s) 1 FlowS-Unet 0.933/0.943 62 2 FCN 0.858/0.873 50 3 U-Net 0.898/0.913 59 4 人工标注 1.000 18 000 
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表 2 多尺度与单尺度训练及预测的F1分数比较
Table 2 The F1 score comparison between multi-scale cross and single-scale training and testing
训练尺度(像素) 预测尺度(像素) F1分数(后处理前/后) 224 224 0.903/0.923 256 256 0.909/0.928 288 288 0.913/0.931 320 320 0.911/0.932 224 0.933/0.939 256 0.938/0.943 多尺度 288 0.939/0.945 320 0.939/0.944 多尺度平均 0.942/0.946
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表 3 FlowS-Unet与其他队伍的F1分数比较
Table 3 The F1 score comparison of FlowS-Unet and other teams
名次 初赛 复赛 决赛 第1名 0.890 0.914 0.861 第2名(FlowS-Unet) 0.903 0.877 0.840 第3名 0.867 0.898 0.800 第4名 0.706 0.870 0.842 第5名 0.879 0.936 0.823
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