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

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.

[1]  Xuelong Li,et al.  Locality Adaptive Discriminant Analysis for Spectral–Spatial Classification of Hyperspectral Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[2]  Min Han,et al.  Remote Sensing Image Transfer Classification Based on Weighted Extreme Learning Machine , 2016, IEEE Geoscience and Remote Sensing Letters.

[3]  Yan Guo,et al.  Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Trevor Darrell,et al.  What you saw is not what you get: Domain adaptation using asymmetric kernel transforms , 2011, CVPR 2011.

[5]  Yicong Zhou,et al.  Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Huanxin Zou,et al.  Unsupervised Cross-View Semantic Transfer for Remote Sensing Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

[7]  Ivor W. Tsang,et al.  Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[9]  Lorenzo Bruzzone,et al.  Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances , 2016, IEEE Geoscience and Remote Sensing Magazine.

[10]  Xuelong Li,et al.  Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Chang Wang,et al.  Heterogeneous Domain Adaptation Using Manifold Alignment , 2011, IJCAI.

[12]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[13]  Shuyang Wang,et al.  Adapting Remote Sensing to New Domain With ELM Parameter Transfer , 2017, IEEE Geoscience and Remote Sensing Letters.

[14]  Hanyun Wang,et al.  Learn Multiple-Kernel SVMs for Domain Adaptation in Hyperspectral Data , 2013, IEEE Geoscience and Remote Sensing Letters.

[15]  Xue Li,et al.  Iterative Reweighting Heterogeneous Transfer Learning Framework for Supervised Remote Sensing Image Classification , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Yakoub Bazi,et al.  Asymmetric Adaptation of Deep Features for Cross-Domain Classification in Remote Sensing Imagery , 2018, IEEE Geoscience and Remote Sensing Letters.

[17]  Yu-Chiang Frank Wang,et al.  Heterogeneous Domain Adaptation and Classification by Exploiting the Correlation Subspace , 2014, IEEE Transactions on Image Processing.

[18]  Li Ma,et al.  Class centroid alignment based domain adaptation for classification of remote sensing images , 2016, Pattern Recognit. Lett..

[19]  Ivor W. Tsang,et al.  Learning with Augmented Features for Heterogeneous Domain Adaptation , 2012, ICML.

[20]  Min Xiao,et al.  Semi-supervised Subspace Co-Projection for Multi-class Heterogeneous Domain Adaptation , 2015, ECML/PKDD.

[21]  Qi Wang,et al.  Optimal Clustering Framework for Hyperspectral Band Selection , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Xuelong Li,et al.  Scene Classification With Recurrent Attention of VHR Remote Sensing Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Trevor Darrell,et al.  Efficient Learning of Domain-invariant Image Representations , 2013, ICLR.

[24]  Shuying Li,et al.  Structure Preserving Transfer Learning for Unsupervised Hyperspectral Image Classification , 2017, IEEE Geoscience and Remote Sensing Letters.