高光谱图像混合像元多维卷积网络协同分解法  

Multi-dimensional convolutional network collaborative unmixing method for hyperspectral image mixed pixels

作　　者：刘帅[1,2] 邢光龙 LIU Shuai;XING Guanglong(School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, China)

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004 [2]河北省信息传输与信号处理重点实验室,河北秦皇岛066004

出　　处：《测绘学报》2020年第12期1600-1608,共9页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(61671401);河北省高等学校科学技术研究项目(QN2017146)。

摘　　要：受成像光谱仪性能与复杂地物分布的影响,高光谱图像存在大量的混合像元。传统的基于学习的混合像元分解方法通常都是浅层模型,或缺少对空间、光谱信息的综合应用。本文提出一种多维卷积网络协同的混合像元分解深层模型,采用多种维度卷积网络能更充分利用多种维度语义信息,有利于估计小样本和高维的高光谱图像混合像元丰度。对训练数据进行增广处理,构建光谱维、空间维和立方体3种卷积神经网络;设计了融合层,协同3种卷积神经网络提取特征,“端到端”的估计混合像元丰度值;模型使用了批量归一化、池化和Dropout方法避免过拟合现象。试验结果表明,多维卷积网络协同方法的引入能更有效地提取空-谱特征信息,与其他的卷积网络解混模型相比,估计的混合像元丰度精度有显著提高。Influenced by the performance of imaging spectrometer and the distribution of complex ground objects,hyperspectral images have a large number of mixed pixels.Traditional learning-based unmixing methods are shallow models,or lack of comprehensive use of spatial and spectral information.This paper proposes a collaborative deep model with multi-dimensional convolutional network.Using multi-dimensional convolutional network can make full use of multi-dimensional semantic information,which is better to estimate hyperspectral mixed pixel abundance with small samples.The method augments training data,constructs three kinds of convolutional neural networks:spectral dimension,spatial dimension and cube dimension;the method designs fusion layer to concatenate features with three kinds of convolutional neural networks,and to“end-to-end”estimate of mixed pixel abundance;the model uses batch normalization,pooling and dropout to avoid over fitting phenomenon.The experimental results indicate that the introduction of our proposed method can extract spatial-spectral feature information more effectively.Compared with other convolutional network unmixing models,the accuracy of the estimated mixed pixel abundance is significantly improved.

关 键 词：高光谱解混 卷积神经网络 深度学习 丰度估计 

分 类 号：P237[天文地球—摄影测量与遥感]