Deep fusion of remote sensing data for accurate classification

IEEE Geoscience and Remote Sensing Letters

Volumn 14, Issue 8, 2017, Pages 1253-1257

  Chen, Yushi ^a   Li, Chunyang ^a   Ghamisi, Pedram ^b,c   Jia, Xiuping ^d   Gu, Yanfeng ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^d UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Convolutional neural network (CNN); Data fusion; Deep neural network (DNN); Feature extraction (FE); Hyperspectral image (HSI); Light detection and ranging (LiDAR); Multispectral image (MSI)

Indexed keywords

DATA FUSION; DEEP NEURAL NETWORKS; NEURAL NETWORKS; OPTICAL RADAR;

CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; CONVOLUTIONAL NEURAL NETWORK; HETEROGENEOUS FEATURES; LIGHT DETECTION AND RANGING; MULTI-SENSORY FUSION; NORMALIZATION STRATEGIES; REMOTE SENSING DATA FUSION;

REMOTE SENSING;

EID: 85020375761     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2017.2704625     Document Type: Article

References (15)

1. J. A. Benediktsson and P. Ghamisi, Spectral-Spatial Classification of Hyperspectral Remote Sensing Images. Boston, MA, USA: Artech House, 2015.
2. M. Dalponte, L. Bruzzone, and D. Gianelle, "Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1416-1427, May 2008.
3. E. Puttonen, A. Jaakkola, P. Litkey, and J. Hyyppä, "Tree classification with fused mobile laser scanning and hyperspectral data," Sensors, vol. 11, no. 5, pp. 5158-5182, May 2011.
4. Y. Gu, Q. Wang, X. Jia, and J. A. Benediktsson, "A novel MKL model of integrating LiDAR data and MSI for urban area classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 10, pp. 5312-5326, Oct. 2015.
5. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, May 2015.
6. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification withdeep convolutional neural networks," in Proc. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, 2012, pp. 1106-1114.
7. Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, "Deep feature extraction and classification of hyperspectral images based on convolutional neural networks," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 6232-6251, Oct. 2016.
8. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
9. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
10. G. Hinton, N. Srivastava, A. Krizhevsky, R. R. Salakhutdinov, and I. Sutskever, "Improving neural networks by preventing co-Adaptation of feature detectors," Comput. Sci., vol. 3, no. 4, pp. 212-223, Jul. 2012.
11. V. Nair and G. Hinton, "Rectified linear units improve restricted Boltzmann machines," in Proc. Int. Conf. Mach. Learn., Haifa, Israel, 2010, pp. 807-814.
12. S. Ioffe and C. Szegedy, "Batchnormalization: Acceleratingdeepnetwork training by reducing internal covariate shift," in Proc. Int. Conf. Mach. Learn., Lile, France, 2015, pp. 448-456.
13. M. D. Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, "Extended profiles with morphological attribute filters for the analysis of hyperspectral data," Int. J. Remote Sens., vol. 31, no. 22, pp. 5975-5991, Dec. 2010.
14. P. Ghamisi, B. Hofle, and X. X. Zhu, "Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., to be published.
15. G. M. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol. 70, no. 5, pp. 627-633, May 2004.