Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints

International Journal of Remote Sensing

Volumn 36, Issue 18, 2015, Pages 4724-4747

  Mei, Xiaoguang ^a,b   Ma, Yong ^a   Fan, Fan ^a   Li, Chang ^b   Liu, Chengyin ^b   Huang, Jun ^a   Ma, Jiayi ^a  

^a WUHAN UNIVERSITY   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BINARY CODES; EXTRACTION; FEATURE EXTRACTION; LEARNING SYSTEMS; MARKOV PROCESSES; NUCLEIC ACIDS; SPECTRAL RESOLUTION;

ADVANCED SPACEBORNE THERMAL EMISSION AND REFLECTION RADIOMETER; CLASSIFICATION PERFORMANCE; DEEP BELIEF NETWORK (DBN); FEATURE EXTRACTION METHODS; HIGH SPECTRAL RESOLUTION; MARKOV RANDOM FIELD MODELS; RESTRICTED BOLTZMANN MACHINE; THREE MACHINE LEARNING METHODS;

ENVIRONMENTAL PROTECTION AGENCY;

ACCURACY ASSESSMENT; ARTIFICIAL INTELLIGENCE; ASTER; LAND CLASSIFICATION; MARKOV CHAIN; SATELLITE DATA;

EID: 84943420300     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2015.1079664     Document Type: Article

References (36)

1. E.Aarts,, and J.Korst. 1988. Simulated Annealing and Boltzmann Machines. New York: John Wiley and Sons.
2. R.Beer, 2006. “TES on the Aura Mission: Scientific Objectives, Measurements, and Analysis Overview.” IEEE Transactions on Geoscience and Remote Sensing 44 (5): 1102–1105. doi:10.1109/TGRS.2005.863716.
3. Y.Bengio,, P.Lamblin, D.Popovici, and H.Larochelle. 2007. “Greedy Layer-Wise Training of Deep Networks.” Advances in Neural Information Processing Systems 19: 153.
4. J.Bioucas-Dias,, A.Plaza, G.Camps-Valls, P.Scheunders, N.Nasrabadi, and J.O.C.E.L.Y.N.Chanussot. 2013. “Hyperspectral Remote Sensing Data Analysis and Future Challenges.” IEEE Geoscience and Remote Sensing Magazine 1 (2): 6–36. doi:10.1109/MGRS.2013.2244672.
5. E.J.Candès,, M.B.Wakin, and S.P.Boyd. 2008. “Enhancing Sparsity by Reweighted ℓ1 Minimization.” Journal of Fourier Analysis and Applications 14 (5–6): 877–905. doi:10.1007/s00041-008-9045-x.
6. C.-I.Chang,, S.Chakravarty, H.-M.Chen, and Y.-C.Ouyang. 2009. “Spectral Derivative Feature Coding for Hyperspectral Signature Analysis.” Pattern Recognition 42 (3): 395–408. doi:10.1016/j.patcog.2008.07.016.
7. G.Dahl,, A.-R.Mohamed, and G.E.Hinton. 2010. “Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine.” Advances in Neural Information Processing Systems, 469–477.
8. D.L.Donoho, 2006. “Compressed Sensing.” IEEE Transactions on Information Theory 52 (4): 1289–1306. doi:10.1109/TIT.2006.871582.
9. M.Ehlers,, M.Gähler, and R.Janowsky. 2003. “Automated Analysis of Ultra High Resolution Remote Sensing Data for Biotope Type Mapping: New Possibilities and Challenges.” ISPRS Journal of Photogrammetry and Remote Sensing 57 (5–6): 315–326. doi:10.1016/S0924-2716(02)00161-2.
10. Y.Fang,, Y.Ma, H.Li, K.Liang, S.Wang, and H.Wang. 2013. “Rapid Ultraspectral Matching Method Based on Crosscut Feature Extraction.” Optical Review 20 (3): 259–265. doi:10.1007/s10043-013-0047-9.
11. A.Fischer,, and C.Igel. 2012. “An Introduction to Restricted Boltzmann Machines.” Lecture Notes in Computer Science: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 7441: 14–36. doi:10.1007/978-3-642-33275-3_2.
12. A.Fischer,, and C.Igel. 2014. “Training Restricted Boltzmann Machines: An Introduction.” Pattern Recognition 47 (1): 25–39. doi:10.1016/j.patcog.2013.05.025.
13. A.Ghiyamat,, H.Z.M.Shafri, G.A.Mahdiraji, R.Ashurov, A.R.M.Shariff, and S.Mansour. 2015. “Airborne Hyperspectral Discrimination of Tree Species with Different Ages Using Discrete Wavelet Transform.” International Journal of Remote Sensing 36 (1): 318–342. doi:10.1080/01431161.2014.995272.
14. P.R.Griffiths,, and J.A.De Haseth. 2007. Fourier Transform Infrared Spectrometry. Vol. 171. Hoboken, NJ: John Wiley & Sons. doi:10.1002/9780470106310.ch7.
15. G.E.Hinton,. 2002. “Training Products of Experts by Minimizing Contrastive Divergence.” Neural Computation 14 (8): 1771–1800. doi:10.1162/089976602760128018.
16. G.E.Hinton, 2009. “Deep Belief Networks.” Scholarpedia 4 (5): 5947. doi:10.4249/scholarpedia.5947.
17. G.E.Hinton, 2012. “A Practical Guide to Training Restricted Boltzmann Machines.” In Neural Networks: Tricks of the Trade, 599–619. Berlin: Springer. doi:10.1007/978-3-642-35289-8_32.
18. G.E.Hinton,, and R.R.Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” Science 313 (5786): 504–507. doi:10.1126/science.1127647.
19. P.O.Hoyer, 2004. “Non-negative Matrix Factorization with Sparseness Constraints.” TheJournal of Machine Learning Research 5: 1457–1469. http://dl.acm.org/citation.cfm?id=1005332.1044709.
20. G.Hughes, 1968. “On the Mean Accuracy of Statistical Pattern Recognizers.” IEEE Transactions on Information Theory 14 (1): 55–63. doi:10.1109/TIT.1968.1054102.
21. G.R.Hunt, 1977. “Spectral Signatures of Particulate Minerals in the Visible and near Infrared.” Geophysics 42 (3): 501–513. doi:10.1190/1.1440721.
22. X.Jia,, and J.A.Richards. 1993. “Binary Coding of Imaging Spectrometer Data for Fast Spectral Matching and Classification.” Remote Sensing of Environment 43 (1): 47–53. doi:10.1016/0034-4257(93)90063-4.
23. H.Jiao,, Y.Zhong, and L.Zhang. 2012. “Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data.” IEEE Transactions on Geoscience and Remote Sensing 50 (10): 4085–4104. doi:10.1109/TGRS.2012.2188856.
24. H.Lee,, C.Ekanadham, and A.Y.Ng. 2008. “Sparse Deep Belief Net Model for Visual Area V2.” Advances in Neural Information Processing Systems 20: 873–880.
25. 2E Estimation of Transformation for Non-Rigid Registration.” IEEE Transactions on Signal Processing 63 (5): 1115–1129. doi:10.1109/TSP.2014.2388434.
26. J.Ma,, J.Zhao, Y.Ma, and J.Tian. 2015. “Non-Rigid Visible and Infrared Face Registration via Regularized Gaussian Fields Criterion.” Pattern Recognition 48 (3): 772–784. doi:10.1016/j.patcog.2014.09.005.
27. J.Ma,, J.Zhao, J.Tian, A.L.Yuille, and Z.Tu. 2014. “Robust Point Matching via Vector Field Consensus.” IEEE Transactions on Image Processing 23 (4): 1706–1721. doi:10.1109/TIP.2014.2307478.
28. J.Ma,, H.Zhou, J.Zhao, Y.Gao, J.Jiang, and J.Tian. 2015. “Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming.” IEEE Transactions on Geoscience and Remote Sensing 53 (12): 6469–6481. doi:10.1109/TGRS.2015.2441954.
29. V.Nair,, and G.E.Hinton. 2009. “3D Object Recognition with Deep Belief Nets.” Advances in Neural Information Processing Systems, 1339–1347.
30. S.-E.Qian,, A.B.Hollinger, D.Williams, and D.Manak. 1996. “Fast Three-Dimensional Data Compression of Hyperspectral Imagery Using Vector Quantization with Spectral-Feature-Based Binary Coding.” Optical Engineering 35 (11): 3242–3249. doi:10.1117/1.601062.
31. R.Salakhutdinov,, and I.Murray. 2008. “On the Quantitative Analysis of Deep Belief Networks.” In Proceedings of the 25th International Conference on Machine Learning, 872–879. ACM. doi:10.1145/1390156.1390266.
32. K.Schäfer,, C.Jahn, P.Sturm, B.Lechner, and M.Bacher. 2003. “Aircraft Emission Measurements by Remote Sensing Methodologies at Airports.” Atmospheric Environment 37 (37): 5261–5271. doi:10.1016/j.atmosenv.2003.09.002.
33. G.Schürmann,, K.Schäfer, C.Jahn, H.Hoffmann, M.Bauerfeind, E.Fleuti, and B.Rappenglück. 2007. “The Impact of Nox, CO and VOC Emissions on the Air Quality of Zurich Airport.” Atmospheric Environment 41 (1): 103–118. doi:10.1016/j.atmosenv.2006.07.030.
34. S.Shanmugam,, and P.SrinivasaPerumal. 2014. “Spectral Matching Approaches in Hyperspectral Image Processing.” International Journal of Remote Sensing 35 (24): 8217–8251. doi:10.1080/01431161.2014.980922.
35. G.W.Taylor,, G.E.Hinton, and S.T.Roweis. 2006. “Modeling Human Motion Using Binary Latent Variables.” Advances in Neural Information Processing Systems, 1345–1352.
36. F.Van der Meer, 2006. “The Effectiveness of Spectral Similarity Measures for the Analysis of Hyperspectral Imagery.” International Journal of Applied Earth Observation and Geoinformation 8 (1): 3–17. doi:10.1016/j.jag.2005.06.001.