Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images

Zhou Li; Ma Li

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Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images

机译：基于极限学习机的异构域自适应用于高光谱图像分类

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An extreme learning machine (ELM)-based heterogeneous domain adaptation (HDA) algorithm is proposed for the classification of remote sensing images. In the adaptive ELM network, one hidden layer is used for the source data to provide the random features, whereas two hidden layers are set for target data to produce the random features as well as a transformation matrix. DA is achieved by constraining both the source data and the transformed target data to share the same output weights. Moreover, manifold regularization is adopted to preserve the local geometry of unlabeled target data. The proposed ELM-based HDA (EHDA) method is applied to cross-domain classification of remote sensing images, and the experimental results using multisensor remote sensing images demonstrate the effectiveness of the proposed approach.

机译：提出了一种基于极限学习机（ELM）的异构域自适应（HDA）算法，用于遥感图像的分类。在自适应ELM网络中，一个隐藏层用于源数据以提供随机特征，而两个隐藏层被设置用于目标数据以生成随机特征以及转换矩阵。通过限制源数据和转换后的目标数据以共享相同的输出权重来实现DA。此外，采用流形正则化来保留未标记目标数据的局部几何形状。提出的基于ELM的HDA（EHDA）方法被应用于遥感图像的跨域分类，并且使用多传感器遥感图像的实验结果证明了该方法的有效性。

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《IEEE Geoscience and Remote Sensing Letters》 |2019年第11期|1781-1785|共5页
作者
Zhou Li; Ma Li;
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作者单位

China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China;

China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China|Chinese Acad Sci Key Lab Spectral Imaging Technol Xian 710119 Shaanxi Peoples R China;

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正文语种 eng
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关键词
Manifolds; Hyperspectral imaging; Imaging; Support vector machines; Optimization; Classification; extreme learning machine (ELM); heterogeneous domain adaptation (HDA); manifold regularization; remote sensing;

机译：歧管;高光谱成像;成像;支持向量机;优化;分类;极限学习机（ELM）;异构域适应（HDA）;多重正则化遥感;

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1. Adversarial Discriminative Active Deep Learning for Domain Adaptation in Hyperspectral Images Classification [J] . Saboori Arash, Ghassemian Hassan International journal of remote sensing . 2021,第9a10期

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2. Extreme Learning Machine-Based Traffic Incidents Detection with Domain Adaptation Transfer Learning [J] . ChaimaeElhatri, MohammedTahifa, JaouadBoumhidi Journal of Intelligent Systems . 2017,第4期

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3. Hyperspectral Image Classification Based on Unsupervised Heterogeneous Domain Adaptation CycleGan [J] . WANG Xuesong, LI Yiran, CHENG Yuhu 电子学报（英文版） . 2020,第004期

机译：基于无监督异构域适应加速的高光谱图像分类
4. Boosting for Domain Adaptation Extreme Learning Machines for Hyperspectral Image Classification [C] . Junshi Xia, Naoto Yokoya, Akira Iwasaki IEEE International Geoscience and Remote Sensing Symposium . 2018

机译：促进用于高光谱图像分类的领域自适应极限学习机
5. Iterative Training Sampling and Active Learning Approaches to Hyperspectral Image Classification [D] . Ma, Kenneth Yeonkong. 2021

机译：高光谱图像分类的迭代培训抽样和主动学习方法
6. Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images [O] . Faxian Cao, Zhijing Yang, Jinchang Ren, 2017

机译：用于高光谱图像光谱空间分类的线性与非线性极限学习机
7. Extreme Learning Machine-Based Ensemble Transfer Learning for Hyperspectral Image Classification [O] . Xiaobo Liu, Qiubo Hu, Yaoming Cai, 2020

机译：基于极端学习机的合奏转移学习高光谱图像分类

Extreme Learning Machine-Based Heterogeneous Domain Adaptation for Classification of Hyperspectral Images

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