Abstract
With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bengio Yoshua, Lamblin Pascal, Popovici Dan, Larochelle Hugo (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153
Burges Christopher JC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167
Chen S, Wang H, Xu F, Jin YQ (2016) Target classification using the deep convolutional networks for sar images. IEEE Trans Geosci Remote Sens 54(8):4806–4817
Ciodaro T, Deva D, De Seixas JM and Damazio D (2012) Online particle detection with neural networks based on topological calorimetry information. In: Journal of physics: conference series, vol 368, p 012–030. IOP Publishing
Collobert Ronan, Weston Jason, Bottou Léon, Karlen Michael, Kavukcuoglu Koray, Kuksa Pavel (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537
Farabet Clement, Couprie Camille, Najman Laurent, LeCun Yann (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915–1929
Han J, Zhang D, Cheng G, Guo L, Ren J (2015) Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans Geosci Remote Sens 53(6):3325–3337
Helmstaedter Moritz, Briggman Kevin L, Turaga Srinivas C, Jain Viren, Seung H Sebastian, Denk Winfried (2013) Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500(7461):168–174
Hinton Geoffrey, Deng Li, Dong Yu, Dahl George E, Mohamed Abdel-rahman, Jaitly Navdeep, Senior Andrew, Vanhoucke Vincent, Nguyen Patrick, Sainath Tara N et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. Signal Process Mag, IEEE 29(6):82–97
Hinton RR, Salakhutdinov GE (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS’12), pp 1097–1105
LeCun Yann, Bengio Yoshua, Hinton Geoffrey (2015) Deep learning. Nature 521(7553):436–444
Leung Michael KK, Xiong Hui Yuan, Lee Leo J, Frey Brendan J (2014) Deep learning of the tissue-regulated splicing code. Bioinformatics 30(12):i121–i129
Ma Junshui, Sheridan Robert P, Liaw Andy, Dahl George E, Svetnik Vladimir (2015) Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model 55(2):263–274
McCulloch Warren S, Pitts Walter (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133
Melgani Farid, Bruzzone Lorenzo (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790
Mikolov Tomáš, Deoras Anoop, Povey Daniel, Burget Lukáš and Černockỳ Jan (2011) Strategies for training large scale neural network language models. In Automatic speech recognition and understanding (ASRU) IEEE workshop on, pp 196–201
Nguyen A, Yosinski J and Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In 2015 IEEE conference on computer vision and pattern recognition (CVPR). pp 427–436, June
Omer G, Mutanga O, Abdel-Rahman EM, Adam E (2015) Performance of support vector machines and artificial neural network for mapping endangered tree species using worldview-2 data in dukuduku forest, south africa. IEEE J Sel Top Appl Earth Obs Remote Sens 8(10):4825–4840
Romero A, Gatta C, Camps-Valls G (2016) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362
Sainath Tara N, Mohamed Abdel-rahman, Kingsbury Brian and Ramabhadran Bhuvana (2013) Deep convolutional neural networks for lvcsr. In Acoustics, speech and signal processing (ICASSP), 2013 IEEE international conference on, pp 8614–8618
Sarikaya Ruhi, Hinton Geoffrey E, Deoras Anoop (2014) Application of deep belief networks for natural language understanding. IEEE/ACM Trans Audio, Speech Lang Process 22(4):778–784
Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge
Sutskever Ilya, Martens James, Dahl George and Hinton Geoffrey (2013) On the importance of initialization and momentum in deep learning. In Proceedings of the 30th international conference on machine learning (ICML-13), pp 1139–1147
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems NIPS’14, pp 3104–3112
Tang J, Deng C, Huang GB, Zhao B (2015) Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans Geosci Remote Sens 53(3):1174–1185
Tompson JJ, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in neural information processing systems (NIPS’14), 1799–1807
Tsai C.-Y., Cox D. Are deep learning algorithms easily hackable? http://coxlab.github.io/ostrichinator
Tuia Devis, Volpi Michele, Copa Loris, Kanevski Mikhail, Munoz-Mari Jordi (2011) A survey of active learning algorithms for supervised remote sensing image classification. IEEE J Sel Topics Signal Process 5(3):606–617
Vapnik Vladimir (2013) The nature of statistical learning theory. Springer Science & Business Media, Berlin
Vincent Pascal, Larochelle Hugo, Bengio Yoshua, and Manzagol Pierre-Antoine (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning - ICML ’08, 1096–1103
Yu Y, Li J, Guan H, Wang C (2016) Automated detection of three-dimensional cars in mobile laser scanning point clouds using dbm-hough-forests. IEEE Trans Geosci Remote Sens 54(7):4130–4142
Zhang F, Du B, Zhang L (2016) Scene classification via a gradient boosting random convolutional network framework. IEEE Trans Geosci Remote Sens 54(3):1793–1802
Zhao W, Du S (2016) Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans Geosci Remote Sens 54(8):4544–4554
Acknowledgments
This study is supported by the National Natural Science Foundation of China (No. 41471368 and No. 41571413).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflict of interest.
Ethical standard
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Liu, P., Choo, KK.R., Wang, L. et al. SVM or deep learning? A comparative study on remote sensing image classification. Soft Comput 21, 7053–7065 (2017). https://doi.org/10.1007/s00500-016-2247-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-016-2247-2