Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998. ,
DOI : 10.1109/5.726791
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.1115
Multi-column deep neural networks for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3642-3649, 2012. ,
DOI : 10.1109/CVPR.2012.6248110
Imagenet classification with deep convolutional neural networks, NIPS, 2012. ,
Information fusion in tree classifiers, International Journal of Remote Sensing, vol.22, issue.5, pp.861-869, 2001. ,
The application of artificial neural networks to the analysis of remotely sensed data, International Journal of Remote Sensing, vol.66, issue.3, pp.617-663, 2008. ,
DOI : 10.1080/01431169408954326
Neural maps in remote sensing image analysis, Neural Network Analysis of Complex Scientific Data: Astronomy and Geosciences, pp.389-403, 2003. ,
DOI : 10.1016/S0893-6080(03)00021-2
Kernel-based methods for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.6, pp.1351-1362, 2005. ,
DOI : 10.1109/TGRS.2005.846154
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.8134
Advances in Spectral-Spatial Classification of Hyperspectral Images, Proceedings of the IEEE, vol.101, issue.3, pp.652-675, 2013. ,
DOI : 10.1109/JPROC.2012.2197589
URL : https://hal.archives-ouvertes.fr/hal-00737075
Morphological Attribute Profiles With Partial Reconstruction, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.3, pp.1738-1756, 2016. ,
DOI : 10.1109/TGRS.2015.2488280
URL : https://hal.archives-ouvertes.fr/hal-01246602
Graph-cut-based model for spectral-spatial classification of hyperspectral images, 2014 IEEE Geoscience and Remote Sensing Symposium, pp.3418-3421, 2014. ,
DOI : 10.1109/IGARSS.2014.6947216
URL : https://hal.archives-ouvertes.fr/hal-01011495
Segmentation of remote-sensing images by incremental neural network, Pattern Recognition Letters, vol.26, issue.8, pp.1096-1104, 2005. ,
DOI : 10.1016/j.patrec.2004.10.004
A comparison of texture measures for the per-field classification of Mediterranean land cover, International Journal of Remote Sensing, vol.63, issue.19, pp.3943-3965, 2004. ,
DOI : 10.1080/01431160110076144
A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, vol.61, issue.5, pp.823-870, 2007. ,
DOI : 10.1016/S0034-4257(01)00305-4
Deep Model for Classification of Hyperspectral image using Restricted Boltzmann Machine, Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, ICONIAAC '14, p.35, 2014. ,
DOI : 10.1145/2660859.2660946
Classification of hyperspectral image based on deep belief networks, 2014 IEEE International Conference on Image Processing (ICIP), 2014. ,
DOI : 10.1109/ICIP.2014.7026039
Hyperspectral Image Classification with Convolutional Neural Networks, Proceedings of the 23rd ACM international conference on Multimedia, MM '15, pp.1159-1162, 2015. ,
DOI : 10.1145/2733373.2806306
URL : http://hdl.handle.net/1854/LU-7034491
Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, 2015. ,
DOI : 10.1109/JSTARS.2015.2388577
Deep Learning-Based Classification of Hyperspectral Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.2094-2107, 2014. ,
DOI : 10.1109/JSTARS.2014.2329330
Spectral???spatial classification of hyperspectral images using deep convolutional neural networks, Remote Sensing Letters, vol.6, issue.6, pp.468-477, 2015. ,
DOI : 10.1109/LGRS.2010.2047711
Anastasios Doulamis, and Nikolaos Doulamis Deep supervised learning for hyperspectral data classification through convolutional neural networks, IEEE IGARSS. IEEE, pp.4959-4962, 2015. ,
DOI : 10.1109/igarss.2015.7326945
Spectral???Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.8, pp.4544-4554, 2016. ,
DOI : 10.1109/TGRS.2016.2543748
On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery, International Journal of Remote Sensing, vol.48, issue.11, pp.3368-3379, 2015. ,
DOI : 10.1109/TGRS.2012.2197860
Learning multiscale and deep representations for classifying remotely sensed imagery, ISPRS Journal of Photogrammetry and Remote Sensing, vol.113, pp.155-165, 2016. ,
DOI : 10.1016/j.isprsjprs.2016.01.004
A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks, International Journal of Remote Sensing, vol.37, issue.7, 2016. ,
DOI : 10.1016/j.neuroimage.2014.12.061
Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine, International Journal of Remote Sensing, vol.73, issue.9, pp.3144-3169, 2015. ,
DOI : 10.1109/TIP.2006.887731
Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks, Convolutional neural network based automatic object detection on aerial images, pp.1797-1801, 2014. ,
DOI : 10.1109/LGRS.2014.2309695
Multiview Deep Learning for Land-Use Classification, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.12, pp.2448-2452, 2015. ,
DOI : 10.1109/LGRS.2015.2483680
Machine learning for aerial image labeling, 2013. ,
Neural networks for pattern recognition, 1995. ,
Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.2, pp.952-964, 2015. ,
DOI : 10.1109/TGRS.2014.2330857
A Scalable Tile-Based Framework for Region-Merging Segmentation, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.10, pp.5473-5485, 2015. ,
DOI : 10.1109/TGRS.2015.2422848
Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
DOI : 10.1109/CVPR.2015.7298965
URL : http://arxiv.org/abs/1411.4038
Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014. ,
DOI : 10.1145/2647868.2654889
A coherent interpretation of AUC as a measure of aggregated classification performance, ICML, 2011. ,
Svm-and mrf-based method for accurate classification of hyperspectral images Geoscience and Remote Sensing Letters, IEEE, vol.7, issue.4, pp.736-740, 2010. ,
How transferable are features in deep neural networks?, NIPS, 2014. ,
What is a good evaluation measure for semantic segmentation?, Procedings of the British Machine Vision Conference 2013, p.2013 ,
DOI : 10.5244/C.27.32
Note on the sampling error of the difference between correlated proportions or percentages, Psychometrika, vol.12, issue.2, pp.153-157, 1947. ,
DOI : 10.1007/BF02295996