Morphological Attribute Profile Cube and Deep Random Forest for Small Sample Classification of Hyperspectral Image

Deep learning based methods have made great progress in hyperspectral image classification. However, training a deep learning model often requires a large number of labeled samples, which are not always available in practical applications. In this paper, a simple but innovative classification paradigm to exploit morphological attribute profile cube is proposed to improve the small sample classification performance of hyperspectral image. First, morphological attribute profiles are constructed by applying different morphological filters to hyperspectral image. Morphological attribute profile cubes are then extracted as the feature of a sample. Second, the obtained morphological attribute profile cubes are scanned with multiple scale sliding windows to make full use of the rich spatial-spectral information. Finally, the features after multi-grained scanning are input into a deep forest classifier to complete the classification task. In this way, the proposed method could use a deep network structure to improve the classification accuracy. To demonstrate the effectiveness of the proposed method, the classification experiments are carried on three widely used hyperspectral data sets. The experimental results demonstrate that the proposed method can outperform the conventional semi-supervised methods and the state-of-the-art deep learning based methods. The demo code on the Salinas dataset is released on the page: https://github.com/liubing220524/ MAPC-DRF-HSI.

[1]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[2]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Hao Wu,et al.  Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification , 2018, IEEE Transactions on Image Processing.

[4]  Jing Wang,et al.  Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data , 2017, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC).

[5]  Pengqiang Zhang,et al.  Deep Relation Network for Hyperspectral Image Few-Shot Classification , 2020, Remote. Sens..

[6]  Jon Atli Benediktsson,et al.  Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles , 2012, IEEE Geoscience and Remote Sensing Letters.

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

[8]  Xing Zhao,et al.  Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Licheng Jiao,et al.  Classification of Hyperspectral Images Based on Multiclass Spatial–Spectral Generative Adversarial Networks , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Xiao Xiang Zhu,et al.  Deep Recurrent Neural Networks for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Ji Feng,et al.  Deep Forest: Towards An Alternative to Deep Neural Networks , 2017, IJCAI.

[12]  Heesung Kwon,et al.  Going Deeper With Contextual CNN for Hyperspectral Image Classification , 2016, IEEE Transactions on Image Processing.

[13]  Nikolaos Doulamis,et al.  Deep supervised learning for hyperspectral data classification through convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[14]  Yuan Yan Tang,et al.  Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification , 2019, IEEE Geoscience and Remote Sensing Letters.

[15]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[16]  Qi Wang,et al.  Hyperspectral Band Selection by Multitask Sparsity Pursuit , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Jon Atli Benediktsson,et al.  SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[18]  Jiayi Ma,et al.  Hyperspectral Image Classification With Robust Sparse Representation , 2016, IEEE Geoscience and Remote Sensing Letters.

[19]  Jun Li,et al.  Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Jon Atli Benediktsson,et al.  Hyperspectral Image Classification With Independent Component Discriminant Analysis , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Jon Atli Benediktsson,et al.  Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Jon Atli Benediktsson,et al.  Classification of hyperspectral images with Extended Attribute Profiles and feature extraction techniques , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[23]  Jon Atli Benediktsson,et al.  Deep Learning for Hyperspectral Image Classification: An Overview , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Antonio J. Plaza,et al.  Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[25]  Pedram Ghamisi,et al.  A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Licheng Jiao,et al.  Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Lin Zhu,et al.  Generative Adversarial Networks for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Digvir S. Jayas,et al.  Hyperspectral imaging to classify and monitor quality of agricultural materials , 2015 .

[29]  Shanjun Mao,et al.  Spectral–spatial classification of hyperspectral images using deep convolutional neural networks , 2015 .

[30]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[31]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Ying Li,et al.  Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Xing Chen,et al.  Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images , 2016, J. Sensors.

[34]  Bing Liu,et al.  A semi-supervised convolutional neural network for hyperspectral image classification , 2017 .

[35]  Mercedes Eugenia Paoletti,et al.  Deep learning classifiers for hyperspectral imaging: A review , 2019 .

[36]  Onisimo Mutanga,et al.  Random Forests Unsupervised Classification: The Detection and Mapping of Solanum mauritianum Infestations in Plantation Forestry Using Hyperspectral Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Xiuping Jia,et al.  Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Fan Zhang,et al.  Deep Convolutional Neural Networks for Hyperspectral Image Classification , 2015, J. Sensors.

[41]  Francisco Argüello,et al.  Exploring ELM-based spatial–spectral classification of hyperspectral images , 2014 .

[42]  Antonio J. Plaza,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spectral–Spatial Hyperspectral Image Segmentation Using S , 2022 .

[43]  Antonio Plaza,et al.  Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

[44]  Jiasong Zhu,et al.  3-D Gaussian–Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[45]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[46]  Y. El-Sharkawy,et al.  Hyperspectral imaging: Anew prospective for remote recognition of explosive materials , 2019, Remote Sensing Applications: Society and Environment.

[47]  Guo Cao,et al.  Cascaded Random Forest for Hyperspectral Image Classification , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[48]  Qian Du,et al.  Hyperspectral Image Classification Using Deep Pixel-Pair Features , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[49]  Qian Du,et al.  Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Shanjun Mao,et al.  A deep learning framework for hyperspectral image classification using spatial pyramid pooling , 2016 .

[51]  Ying Wang,et al.  Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation , 2014 .

[52]  Dirk Van,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[53]  Jon Atli Benediktsson,et al.  A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Lin Zhu,et al.  Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network , 2017, IEEE Geoscience and Remote Sensing Letters.