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Classifier mechanism embedded feature-extraction method for hyperspectral images

  • role： First author第一作者
  • Affiliation：

    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116

  • Email：xingcd_nuaa@163.com
  • Introduction：邢长达， 研究方向为基于机器学习的遥感图像智能分析。E-mail： xingcd_nuaa@163.com
XING Changda1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106

  • Email：mely_nuaa@163.com
  • Introduction：汪美玲， 研究方向为基于机器学习的智能计算。E-mail： mely_nuaa@163.com
WANG Meiling2*,  
  • Affiliation：

    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106

XU Yongchang3,  
  • Affiliation：

    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106

WANG Zhisheng3

Resümee

As an important technique in image interpretation, hyperspectral image (HSI) classification is extensively used in many fields, such as remote-sensing observation and intelligent medical service. HSI classification may comprise label prediction based on feature extraction and based on classifiers. Although deep learning can directly obtain the classification results by one step, which is achieved by the end-to-end network structure from data input to classification result output, they are actually viewed as a direct cascade of both feature extraction based on deep networks (such as deep autoencoder and convolutional neural network) and classifiers (such as softmax and logistic regression). Most current classification approaches do not consider the influence of classifiers on feature extraction, which may cause the incompatibility between the extracted features and the used classifier. This incompatibility is reflected in the poor matching relationship between the classifier model and its input feature data, leading to poor prediction results.Method To remedy such deficiency, this paper presents a novel kind of HSI feature-extraction methods embedded by the classifier mechanism, which can ensure the compatibility between feature extraction and the used classifier. Thus, the features can be more easily calculated by classifier accurately, and classification prediction results can be improved. Two specific forms are given in this paper. 1) The sparse representation (SR)feature-extraction model compatible with Support Vector Machine (SVM) classifier is built, which embeds the SVM property into the SR. 2) The deep autoencoder (DAE) feature-extraction model compatible with softmax classifier is constructed, which integrates the softmax function into DAE network. We also provide the optimization strategy to obtain the optimal solutions of the SR and DAE models.Results The proposed SR and DAE models are experimentally evaluated on the remote-sensing HSI data and medical HSI data. The experiments consist of parameter analysis, algorithm comparison, ablation study, and convergence analysis. According to the parameter analysis, we validate that the values of important parameters have obvious impact on the performance of our methods and successfully select the best values of these parameters. As suggested by the algorithm comparison, the proposed methods achieve better classification performance than some state-of-the-art approaches, which have obvious effectiveness and superiority. The overall accuracy, average accuracy, and Kappa indices in the HSI classification task are, on average, higher by 5.03%, 5.13%, and 7.30%, respectively. An ablation study is conducted to demonstrate the effectiveness of the compatibility between feature extraction and the bedded classifiers for the performance improvement of HSI classification. Convergence analysis indicates that the designed optimization-solution strategy can meet the application requirements of reliability and rapidity.Conclusion The proposed SR and DAE methods realize good compatibility between feature extraction and classifiers. Accordingly, the extracted features can be better calculated by classifiers, and more competitive classification performance can be achieved.

Schlüsselwort

hyperspectral image classification;feature extraction;classifier mechanism;sparse representation;deep autoencoder network

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