摘要
基于遥感图像中的光学信号检测出一定时间内特定区域的变化状态的遥感图像变化检测方法,在国防安全、环境监测、城市建设等领域具有重要应用价值。由于多时相异源图像在成像机理、光谱范围、空间分辨率等方面存在差异,现阶段异源遥感图像变化检测仍存在精度不够高、漏检和误检等问题,本文提出一种基于Transformer网络的异源变化检测网络框架,该框架能够利用不同类别的异源遥感图像获得准确的变化检测结果。首先,所提出检测网络为多时相遥感图像自适应生成对应的光学信号Token (光信Token);然后,以光信Token作为引导与对应图像块Token进行交互计算,从而对双时相序列特征进行变化分析,并且在交互学习过程中构建了差分放大模块以提高网络对特征间差分信息的提取精度;最后,利用多层感知机对输出的差分Token进行预测并分割出变化区域。采用Sardinia、Shuguang和Bastrop等3个不同类别的异源遥感图像数据集和Farmland同源高光谱图像数据集来验证本文提出的方法,结果证明在选取有限训练样本数据情况下,本文方法与现有主流变化检测方法相比,在多个客观指标以及主观视觉上都表现出先进性。
Change Detection(CD)is a vital technique for identifying and analyzing changes over time in a specific area using optical signals from remote sensing images.This technique has been extensively utilized in various fields,including national defense security,environmental monitoring,and urban construction.However,some challenges in achieving accurate and reliable CD are still encountered due to inherent disparities in imaging mechanisms,spectral ranges,and spatial resolutions among heterogeneous images.These challenges lead to issues such as inadequate accuracy,missed detections,and false detections.Heterogeneous remote sensing images can be regarded as sequences of different optical signals from the channel perspective.For example,RGB and infrared images can be regarded as sequences of spectral signals from different ranges.Transformers employ a multi-head attention mechanism that can effectively handle and analyze sequence information to achieve accurate heterogeneous CD.Thus,the paper proposes an optical signal token guided CD network for heterogeneous remote sensing images.This paper presents a novel heterogeneous CD network,primarily comprising the optical-signal token transformer(OT-Former)and the cross-temporal transformer(CT-Former).The proposed method demonstrates the capacity to effectively handle diverse remote sensing images of distinct categories and attain precise CD results.Specifically,OT-Former can encode diverse heterogeneous images in channelwise for adaptively generating the optical-signal tokens.Meanwhile,CT-Former can use the optical-signal tokens as a guide to interact with the patch token for the learning of change rules.Moreover,a Difference Amplification Module(DAM)is embedded into the network to enhance the extraction of difference information.This module utilizes a 1×2 convolutional kernel to effectively fuse difference information.Finally,the differential token is predicted by multilayer perceptron to output the CD results.Experiments were conducted on three heterogeneous datasets and o
作者
刘秦森
孙帮勇
LIU Qinsen;SUN Bangyong(Faculty of Printing,Packaging Engineering and Digital Media Technology,Xi’an University of Technology,Xi’an 710054,China;State Key Laboratory of Transient Optics and Photonics,Xi’an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences,Xi’an 710119,China)
出处
《遥感学报》
EI
CSCD
北大核心
2024年第1期88-104,共17页
NATIONAL REMOTE SENSING BULLETIN
基金
国家自然科学基金(编号:62076199)
陕西省重点研发计划(编号:2022ZDLGY01-03)
瞬态光学与光子技术国家重点实验室开放基金(编号:SKLST202214)
陕西省教育厅重点科学研究计划(编号:23JY063)。
