SVM or deep learning? A comparative study on remote sensing image classification
- 1.8k Downloads
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.
KeywordsSpatial big data Sparse auto-encoder Support vector machine Active learning Remote sensing
This study is supported by the National Natural Science Foundation of China (No. 41471368 and No. 41571413).
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bengio Yoshua, Lamblin Pascal, Popovici Dan, Larochelle Hugo (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153Google Scholar
- 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):4806–4817Google Scholar
- 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 PublishingGoogle Scholar
- 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):82–97CrossRefGoogle Scholar
- Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS’12), pp 1097–1105Google Scholar
- 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 196–201Google Scholar
- 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, JuneGoogle Scholar
- Romero A, Gatta C, Camps-Valls G (2016) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362Google Scholar
- 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 8614–8618Google Scholar
- Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, CambridgeGoogle Scholar
- 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 1139–1147Google Scholar
- Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems NIPS’14, pp 3104–3112Google Scholar
- 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), 1799–1807Google Scholar
- Tsai C.-Y., Cox D. Are deep learning algorithms easily hackable? http://coxlab.github.io/ostrichinator
- 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, 1096–1103Google Scholar
- 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):4130–4142Google Scholar
- 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):4544–4554Google Scholar