Deep Feature Learning for Hyperspectral Image Classification and Land Cover Estimation

The differences in spatial sampling between field measurements and remote-sensing imagery can hinder the exploitation of contemporary data. When the field-based sampling is higher than airborne and spaceborne imagery, each pixel is naturally associated with multiple pixels due to the multiplexing of the reflectances of different materials. To address this scale inconsistency, we propose the introduction of the multi-label classification framework where classifiers are trained to predict multiple labels per pixel. Furthermore, instead of relying on raw hyperspectral measurements for the classification process, we investigate the Stacked Sparse Autoencoders framework, an example of a deep learning network, for descriptive feature extraction. To validate the merits of the proposed scheme, we consider real data from the Hyperion instrument on-board the EO-1 and NYC land cover data from 2010.

[1]  Min-Ling Zhang,et al.  A Review on Multi-Label Learning Algorithms , 2014, IEEE Transactions on Knowledge and Data Engineering.

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

[3]  Nikos Komodakis,et al.  Building detection in very high resolution multispectral data with deep learning features , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[4]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[5]  R. Fergus,et al.  Learning invariant features through topographic filter maps , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[7]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[8]  Geoff Holmes,et al.  Classifier chains for multi-label classification , 2009, Machine Learning.

[9]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[10]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[11]  Jefersson Alex dos Santos,et al.  Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Andreas E. Savakis,et al.  Sparse Representations and Distance Learning for Attribute Based Category Recognition , 2010, ECCV Workshops.

[13]  Amy Loutfi,et al.  Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks , 2016, Remote. Sens..

[14]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

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

[16]  Panagiotis Tsakalides,et al.  Land Classification Using Remotely Sensed Data: Going Multilabel , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Yanfei Zhong,et al.  Large patch convolutional neural networks for the scene classification of high spatial resolution imagery , 2016 .

[19]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[20]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[21]  Quoc V. Le,et al.  Measuring Invariances in Deep Networks , 2009, NIPS.

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

[23]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[25]  Yansheng Li,et al.  Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[26]  Michalis Zervakis,et al.  Deep learning for multi-label land cover classification , 2015, SPIE Remote Sensing.

[27]  Marc'Aurelio Ranzato,et al.  Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.

[28]  Pascal Vincent,et al.  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.

[29]  Panagiotis Tsakalides,et al.  Low Light Image Enhancement via Sparse Representations , 2014, ICIAR.