双通道孪生网络在遥感变化检测中的应用  

2-channel Siamese Network for Remote Sensing Change Detection

作　　者：郑思宇 胡华浪 黄进[1] 付国栋 杨旭[1] 王敏[1] 李剑波 秦泽宇 ZHENG Si-Yu;HU Hua-Lang;HUANG Jin;FU Guo-Dong;YANG Xu;WANG Min;LI Jian-Bo;QIN Ze-Yu(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学电气工程学院,成都611756

出　　处：《计算机系统应用》2022年第3期56-64,共9页Computer Systems & Applications

摘　　要：遥感变化检测旨在通过比较同一位置不同时期遥感影像以检测出存在显著或者潜在的遥感变化的区域.目前,大多数相关工作聚焦于正时序变化检测上,对反时序变化检测表现较差.为了避免时序对变化检测带来影响,通常的做法是将正时序和反时序的数据都囊括构成数据集.然而,这会导致模型训练时间更长.本文提出一种双通道孪生网络,在保证模型精度的同时实现高效的模型训练.首先,本文将现有模型改造为对称的结构,使之既能仅采用原始正时序数据集快速训练,又能同时学习正时序和反时序变化的特征信息.然后,本文提出了双通道孪生输入以获取更加鲁棒的特征.最后,本文加入了双通道孪生融合对特征进行精炼.在Onera Satellite Change Detection Sentinel-2数据集上,所提出的模型,在精度和训练有效性都超过了现有模型.进一步的消融实验验证了本文模型中两个模块的性能.Remote sensing change detection aims to compare multi-temporal remote sensing images at the same location and identify significant as well as potential changes between them.Most of the related work focuses on the chronological changes but performs poorly on anti-chronological detection.To avoid temporal effect,a common approach is to involve both chronological and anti-chronological data into datasets,but the model training time would be doubled simultaneously.Therefore,this study proposes a 2-channel siamese network to ensure high accuracy as well as efficient training at the same time.Firstly,a symmetric model is constructed based on existing models to achieve fast training only using the original chronological datasets and to learn both chronological and anti-chronological features.Next,2-channel siamese input model is designed to wrap the inputs for more robust feature extraction.Finally,attention mechanism is applied to further fuse and refine the extracted features.The proposed method is evaluated on the Onera Satellite Change Detection Sentinel-2 dataset.The proposed model outperforms several existing models in terms of both accuracy and training validity.A further ablation study verifies the efficacy of proposed models.

关 键 词：遥感变化检测 时序影响 反时序变化检测 对称模型 双通道孪生框架 注意力机制 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]