基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测

刘宣广; 李蒙蒙; 汪小钦; 张振超

doi:10.11834/jrs.20221627

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专辑

基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测
Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images
2024年28卷第2期页码：437-454
纸质出版日期： 2024-02-07 ，
DOI： 10.11834/jrs.20221627
稿件说明：

移动端阅览

刘宣广，李蒙蒙，汪小钦，张振超.2024.基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测.遥感学报，28（2）： 437-454

Liu X G，Li M M，Wang X Q and Zhang Z C. 2024. Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images. National Remote Sensing Bulletin， 28（2）：437-454
刘宣广，李蒙蒙，汪小钦，张振超.2024.基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测.遥感学报，28（2）： 437-454 DOI： 10.11834/jrs.20221627.

Liu X G，Li M M，Wang X Q and Zhang Z C. 2024. Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images. National Remote Sensing Bulletin， 28（2）：437-454 DOI： 10.11834/jrs.20221627.

摘要

建筑物变化检测在城市环境监测、土地规划管理和违章违规建筑识别等应用中具有重要作用。针对传统孪生神经网络在影像变化检测中存在的检测边界与实际边界吻合度低的问题，本文结合面向对象图像分析技术，提出一种基于面向对象孪生神经网络（Obj-SiamNet）的高分辨率遥感影像变化检测方法，利用模糊集理论自动融合多尺度变化检测结果，并通过生成对抗网络实现训练样本迁移。该方法应用在高分二号和高分七号高分辨率卫星影像中，并与基于时空自注意力的变化检测模型（STANet）、视觉变化检测网络（ChangeNet）和孪生UNet神经网络模型（Siam-NestedUNet）进行比较。结果表明：（1）融合面向对象多尺度分割的检测结果较单一尺度分割的检测结果，召回率最高提升32%，F1指数最高提升25%，全局总体误差（GTC）最高降低7%；（2）在样本数量有限的情况下，通过生成对抗网络进行样本迁移，与未使用样本迁移前的检测结果相比，召回率最高提升16%，F1指数最高提升14%，GTC降低了9%；（3）Obj-SiamNet方法较其他变化检测方法，整体检测精度得到提升，F1指数最高提升23%，GTC最高降低9%。该方法有效提高了建筑物变化检测在几何和属性方面的精度，并能有效利用开放地理数据集，降低了模型训练样本制作成本，提升了检测效率和适用性。

Abstract

Building change detection is essential to many applications

such as monitoring of urban areas

land use management

and illegal building detection. It has been seen as an effective means to detect building changes from remote-sensing images.

This paper proposes an object-based Siamese neural network

labeled as Obj-SiamNet

to detect building changes from high-resolution remote-sensing images. We combine the advantages of object-based image analysis methods and Siamese neural networks to improve the geometric accuracies of detected boundaries. Moreover

we implement the Obj-SiamNet at multiple segmentation levels and automatically construct a set of fuzzy measures to fuse the obtained results at multi-levels. Furthermore

we use generative adversarial methods to generate target-like training samples from publicly available datasets and construct a relatively sufficient training dataset for the Obj-SiamNet model. Finally

we apply the proposed method into three high-resolution remote-sensing datasets

i.e.

a GF-2 image-pair in Fuzhou City

and a GF2 image pair in Pucheng County

and a GF-2—GF-7 image pair in Quanzhou City. We also compare the proposed method with three other existing ones

namely

STANet

ChangeNet

and Siam-NestedUNet.

Experimental results show that the proposed method performs better than the other three in terms of detection accuracy. (1) Compared with the detection results from single-scale segmentation

the detection results from multi-scale increases the recall rate by up to 32%

the F1-Score increases by up to 25%

and the Global Total Classification error (GTC) decreases by up to 7%. (2) When the number of available samples is limited

the adopted Generative Adversarial Network (GAN) is able to generate effective target-like samples for diverting samples. Compared with the detection without using GAN-generated samples

the proposed detection increases the recall rate by up to 16%

increases the F1-Score by up to 14%

and decreases GTC by 9%. (3) Compared with other change-detection methods

the proposed method improves the detection accuracies significantly

i.e.

the F1-Score increases by up to 23%

and GTC decreases by up to 9%. Moreover

the boundaries of the detected changes by the proposed method have a high consistency with that of ground truth.

We conclude that the proposed Obj-SiamNet method has a high potential for building change detection from high-resolution remote-sensing images.

关键词

遥感变化检测孪生神经网络面向对象多尺度分析模糊集融合生成对抗网络高分辨率遥感影像

Keywords

change detection of remote sensingSiamese Neural Networkobject-based multi-scale analysisfuzzy sets fusiongenerative adversarial networkvery high resolution remote sensing images

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