Interactive Domain Adaption for the Classification of Remote Sensing Images Using Active Learning
Keywords:
Active learning (AL), domain adaptation (DA), image classification, support vector machine (SVM)Abstract
Interactive Domain Adaptation (IDA) technique based on active learning for the classification of remote sensing images. Interactive domain adaptation method is used for adapting the supervised classifier trained on a given remote sensing source image to make it suitable for classifying a different but related target image. The two images can be acquired in different locations and at different times. This method iteratively selects the most informative samples of the target image to be labeled and included in the training set and the source image samples are re weighted or removed from the training set on the basis of their disagreement with the target image classification problem. The consistent information available from the source image can be effectively exploited for the classification of the target image and for guiding the selection of new samples to be labeled, whereas the inconsistent information is automatically detected and removed. This approach significantly reduces the number of new labeled samples to be collected from the target image. Experimental results on both a multi spectral very high resolution and a hyper spectral data set confirm the effectiveness of the interactive domain adaptation for the classification of remote sensing using active learning method.
References
P. Mitra, B. U. Shankar, and S. K. Pal,“Segmentation of multispectral remote sensing images using active support vector machines,”
Pattern Recognit. Lett. vol. 25, no. 9, pp. 1067–1074, Jul. 2004.
S. Rajan, J. Ghosh, and M. Crawford, “An active learning approach to hyperspectral data classification,” IEEE Trans. Geosci. Remote Sens.,vol. 46, no. 4, pp. 1231–1242, Apr. 2008.
G. Jun and J. Ghosh, “An efficient active learning algorithm with knowledge transfer for hyperspectral data analysis,” in Proc. IEEE IGARSS,Jul. 2008, vol. 1, pp. I-52–I-55.
B. Demir, C. Persello, and L. Bruzzone, “Batchmode active-learningmethods for the interactive classification of remote sensing images,”IEEETrans. Geosci. Remote Sens., vol. 49, no. 3, pp. 1014–1031, Mar. 2011.
J. Huang, A. Gretton, B. Schölkopf, A. J. Smola, and K. M. Borgwardt,“Correcting sample selection bias by unlabeled data,” in Proc. Advancesin Neural Information Processing Systems. Cambridge,MA: MIT Press, 2007.
D. Tuia, E. Pasolli, and W. J. Emery, “Using active learning to adaptremote sensing image classifiers,” Remote Sens. Environ., vol. 115, no. 9, pp. 2232–2242, Sep. 2011.
W. Dai, Q. Yang, G. Xue, and Y. Yu, “Boosting for transfer learning,” inProc. Int. Conf. Mach. Learn., 2007, pp. 193–200.
C. Persello and L. Bruzzone, “Active learning for domain adaptation in thesupervised classification of remote sensing images,” IEEE Trans. Geosci.Remote Sens., vol. 50, no. 11, pp. 4468–4483, Nov. 2012.
B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2001.
F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans.Geosci. RemoteSens., vol. 42, no. 8, pp. 1778–1790, Aug. 2004.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
