Active learning based autoencoder for hyperspectral imagery classification

In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that driven by active learning, the performance of autoencoder can be greatly improved.

[1]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[4]  Antonio J. Plaza,et al.  Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression , 2013, IEEE Geoscience and Remote Sensing Letters.

[5]  William J. Emery,et al.  Active Learning Methods for Remote Sensing Image Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[7]  Begüm Demir,et al.  Clustering-Based Extraction of Border Training Patterns for Accurate SVM Classification of Hyperspectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

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

[9]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[10]  John Shepanski,et al.  Hyperion, a space-based imaging spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

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

[13]  Erzsébet Merényi,et al.  Classification of hyperspectral imagery with neural networks: comparison to conventional tools , 2014, EURASIP Journal on Advances in Signal Processing.

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

[15]  Antonio J. Plaza,et al.  Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning , 2011, IEEE Transactions on Geoscience and Remote Sensing.