Hyperspectral Image Classification Based on Domain Adaptation Broad Learning

Hyperspectral images (HSI) are widely applied in numerous fields for their rich spatial and spectral information. However, in these applications, we always face the situation that the available labeled samples are limited or absent. Therefore, we propose an HSI classification method based on domain adaptation broad learning (DABL). First, according to the importance of the marginal and conditional distributions, the maximum mean discrepancy is used in mapped features to adapt these distributions between source and target domains. Meanwhile the manifold regularization is added to maintain the manifold structure of the input HSI data. Second, to further reduce the distribution difference and maintain manifold structure, the domain adaptation and manifold regularization are added to the output layer of DABL. Finally, the output weights can be easily calculated by the ridge regression theory. Experimental results on three real HSI datasets demonstrate the effectiveness of our proposed DABL.

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

[2]  Lorenzo Bruzzone,et al.  Support vector machines for classification of hyperspectral remote-sensing images , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[3]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[4]  Qingshan Liu,et al.  Matrix-Based Discriminant Subspace Ensemble for Hyperspectral Image Spatial–Spectral Feature Fusion , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Lorenzo Bruzzone,et al.  Domain adaptation based on deep denoising auto-encoders for classification of remote sensing images , 2016, Remote Sensing.

[6]  Koby Crammer,et al.  Analysis of Representations for Domain Adaptation , 2006, NIPS.

[7]  Jocelyn Chanussot,et al.  Multiple Kernel Learning for Hyperspectral Image Classification: A Review , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Hui Lin,et al.  Classification of Hyperspectral Images by Gabor Filtering Based Deep Network , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Jinping Wang,et al.  Texture Pattern Separation for Hyperspectral Image Classification , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Cheng Wu,et al.  Domain Space Transfer Extreme Learning Machine for Domain Adaptation , 2019, IEEE Transactions on Cybernetics.

[11]  Sethuraman Panchanathan,et al.  A Two-Stage Weighting Framework for Multi-Source Domain Adaptation , 2011, NIPS.

[12]  Qian Du,et al.  GPU Parallel Implementation of Support Vector Machines for Hyperspectral Image Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Wei Li,et al.  Diverse Region-Based CNN for Hyperspectral Image Classification , 2018, IEEE Transactions on Image Processing.

[14]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Jon Atli Benediktsson,et al.  Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Saharon Rosset,et al.  Leakage in data mining: formulation, detection, and avoidance , 2011, TKDD.

[17]  Lorenzo Bruzzone,et al.  Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Gokhan Bilgin,et al.  Semisupervised Hyperspectral Image Classification Using Deep Features , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  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.

[20]  Huanxin Zou,et al.  Transfer Sparse Subspace Analysis for Unsupervised Cross-View Scene Model Adaptation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[22]  Saurabh Prasad,et al.  Deep Feature Alignment Neural Networks for Domain Adaptation of Hyperspectral Data , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[23]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[24]  Victor S. Lempitsky,et al.  Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.

[25]  Naif Alajlan,et al.  Domain Adaptation Network for Cross-Scene Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[26]  C. L. Philip Chen,et al.  Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System , 2018, Remote. Sens..

[27]  Olivier Leo,et al.  Evaluating NDVI Data Continuity Between SPOT-VEGETATION and PROBA-V Missions for Operational Yield Forecasting in North African Countries , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Pabitra Mitra,et al.  BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Gustau Camps-Valls,et al.  Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization , 2016, ArXiv.

[30]  Qian Du,et al.  Discriminative Transfer Joint Matching for Domain Adaptation in Hyperspectral Image Classification , 2019, IEEE Geoscience and Remote Sensing Letters.

[31]  Joydeep Ghosh,et al.  An Active Learning Approach to Hyperspectral Data Classification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Naoto Yokoya,et al.  Ensemble of transfer component analysis for domain adaptation in hyperspectral remote sensing image classification , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[33]  C. L. Philip Chen,et al.  Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Xue Li,et al.  On Gleaning Knowledge From Cross Domains by Sparse Subspace Correlation Analysis for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Gustavo Camps-Valls,et al.  Semisupervised Classification of Remote Sensing Images With Active Queries , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[36]  C. L. Philip Chen,et al.  Discriminative graph regularized broad learning system for image recognition , 2018, Science China Information Sciences.

[37]  Shuang Feng,et al.  Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification , 2020, IEEE Transactions on Cybernetics.

[38]  Philip S. Yu,et al.  Visual Domain Adaptation with Manifold Embedded Distribution Alignment , 2018, ACM Multimedia.

[39]  Saurabh Prasad,et al.  Domain Adaptation for Robust Classification of Disparate Hyperspectral Images , 2017, IEEE Transactions on Computational Imaging.

[40]  Lorenzo Bruzzone,et al.  Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Yun Ding,et al.  Robust Spatial–Spectral Block-Diagonal Structure Representation With Fuzzy Class Probability for Hyperspectral Image Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Mohammad Sadegh Helfroush,et al.  Sparse-Based Classification of Hyperspectral Images Using Extended Hidden Markov Random Fields , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  Bo Du,et al.  Domain Adaptation With Discriminative Distribution and Manifold Embedding for Hyperspectral Image Classification , 2019, IEEE Geoscience and Remote Sensing Letters.

[44]  Peijun Du,et al.  Spectral–Spatial Rotation Forest for Hyperspectral Image Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  Philip S. Yu,et al.  Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.