Hyperspectral Image Classification Using Deep Pixel-Pair Features

The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Bo Du,et al.  A Discriminative Metric Learning Based Anomaly Detection Method , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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

[4]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[6]  Bo Du,et al.  Saliency-Guided Unsupervised Feature Learning for Scene Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[7]  James E. Fowler,et al.  Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Bo Du,et al.  Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.

[9]  Qian Du,et al.  Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  James E. Fowler,et al.  Decision Fusion in Kernel-Induced Spaces for Hyperspectral Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[11]  James E. Fowler,et al.  Locality-Preserving Discriminant Analysis in Kernel-Induced Feature Spaces for Hyperspectral Image Classification , 2011, IEEE Geoscience and Remote Sensing Letters.

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

[13]  Shihong Du,et al.  Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Jun Li,et al.  ${{\rm E}^{2}}{\rm LMs}$ : Ensemble Extreme Learning Machines for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[16]  Qian Du,et al.  Combined sparse and collaborative representation for hyperspectral target detection , 2015, Pattern Recognit..

[17]  Tong Zhang,et al.  Deep Learning Based Feature Selection for Remote Sensing Scene Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[18]  Qian Du,et al.  Collaborative-Representation-Based Nearest Neighbor Classifier for Hyperspectral Imagery , 2015, IEEE Geoscience and Remote Sensing Letters.

[19]  Farid Melgani,et al.  Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Carlo Gatta,et al.  Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Antonio J. Plaza,et al.  Subspace-Based Support Vector Machines for Hyperspectral Image Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[22]  Liangpei Zhang,et al.  An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Jon Atli Benediktsson,et al.  Generalized Composite Kernel Framework for Hyperspectral Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[25]  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.

[26]  Jie Geng,et al.  Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Nasser M. Nasrabadi,et al.  Hyperspectral Target Detection : An Overview of Current and Future Challenges , 2014, IEEE Signal Processing Magazine.

[28]  Jon Atli Benediktsson,et al.  Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Ye Zhang,et al.  Robust Hyperspectral Classification Using Relevance Vector Machine , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[31]  Jie Geng,et al.  Hyperspectral image classification via contextual deep learning , 2015, EURASIP Journal on Image and Video Processing.