References
Blei, D.M., A.Y. Ng, and M.I. Jordan, 2003. Latent Dirichlet
Allocation,
Journal of Machine Learning Research
, 3(Jan):993–
1022.
Blum, M., S. Jost Tobias, J. Wülfing, and M. Riedmiller, 2012. A
learned feature descriptor for object recognition in RGB-D data,
Proceedings of the 2012 IEEE International Conference on
Robotics and Automation
, pp. 1298–1303.
Cabrera, C., R. Artacho, and R. Giménez, 2006. Beneficial effects of
green tea-A review,
Journal of the American College of Nutrition
,
25(2):79–99.
Cheriyadat, A.M., 2014. Unsupervised feature learning for aerial
scene classification,
IEEE Transactions on Geoscience and
Remote Sensing
, 52(1):439–451.
Chong, W., D. Blei, and F.F. Li, 2009. Simultaneous image
classification and annotation,
Proceedings of the 2009 IEEE
Conference on Computer Vision and Pattern Recognition
, 20-25
June 2009, pp. 1903–1910.
Coates, A., H. Lee, and A.Y. Ng, 2010. An analysis of single-layer
networks in unsupervised feature learning,
Ann Arbor
,
1001(48109):2.
Damodaran, B.B., J. Höhle, and S. Lefèvre, 2017. Attribute profiles
on derived features for urban land cover classification,
Photogrammetric Engineering & Remote Sensing
, 83(3):183–193.
Dosovitskiy, A., J.T. Springenberg, M. Riedmiller, and T. Brox, 2014.
Discriminative unsupervised feature learning with convolutional
neural networks,
Advances in Neural Information Processing
Systems
, 2014:766–774.
Dutta, R., A. Stein, E.M.A. Smaling, R.M. Bhagat, and M. Hazarika,
2010. Effects of plant age and environmental and management
factors on tea yield in northeast India,
Agronomy Journal
,
102(4):1290–1301.
Hofmann, T., 2001. Unsupervised learning by probabilistic latent
semantic analysis,
Machine Learning
, 42(1):177–196.
Huang, X., and L. Zhang, 2013. An SVM ensemble approach
combining spectral, structural, and semantic features for the
classification of high-resolution remotely sensed imagery,
IEEE
Transactions on Geoscience and Remote Sensing
, 51(1):257–272.
Huang, X., Q. Lu, and L. Zhang, 2014. A multi-index learning
approach for classification of high-resolution remotely sensed
images over urban areas,
ISPRS Journal of Photogrammetry and
Remote Sensing
, 90:36–48.
Huang, X., H. Liu, and L. Zhang, 2015. Spatiotemporal detection and
analysis of urban villages in mega city regions of China using
high-resolution remotely sensed imagery,
IEEE Transactions on
Geoscience and Remote Sensing
, 53(7):3639–3657.
Huang, X., D. Wen, J. Li, and R. Qin, 2017. Multi-level monitoring of
subtle urban changes for the megacities of China using high-
resolution multi-view satellite imagery,
Remote Sensing of
Environment
, 196:56–75.
Jon, D.M., and M.B. David, 2008. Supervised topic models,
Advances
in Neural Information Processing Systems
, 121–128.
Lee, T.S., 1996. Image representation using 2D Gabor wavelets,
IEEE
Transactions on Pattern Analysis and Machine Intelligence
,
18(10):959–971.
Li, Y., C. Tao, Y. Tan, K. Shang, and J. Tian, 2016. Unsupervised
multilayer feature learning for satellite image scene
classification,
IEEE Geoscience and Remote Sensing Letters
,
13(2):157–161.
Lienou, M., H. Maitre, and M. Datcu, 2010. Semantic annotation
of satellite images using latent Dirichlet allocation,
IEEE
Geoscience and Remote Sensing Letters
, 7(1):28–32.
Ming, D., X. Zhang, M. Wang, and W. Zhou, 2016. Cropland
extraction based on OBIA and adaptive scale pre-estimation,
Photogrammetric Engineering & Remote Sensing
, 82(8):635–644.
Pesaresi, M., 2008. Textural analysis of coca plantations using
remotely sensed data with resolution of 1 metre,
International
Journal of Remote Sensing
, 29(23):6985–7002.
Putthividhy, D., H.T. Attias, and S.S. Nagarajan, 2010. Topic
regression multi-modal Latent Dirichlet Allocation for image
annotation,
Proceedings of the 2010 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition,
13-18
June 2010, pp. 3408–3415.
Qin, R., 2015. A mean shift vector-based shape feature for
classification of high spatial resolution remotely sensed imagery,
IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing
, 8(5):1974–1985.
Qin, Y., X. Xiao, J. Dong, Y. Zhou, Z. Zhu, G. Zhang, G. Du, C. Jin, W.
Kou, J. Wang, and X. Li, 2015. Mapping paddy rice planting area
in cold temperate climate region through analysis of time series
Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery,
ISPRS
Journal of Photogrammetry and Remote Sensing
, 105:220–233.
Reis, S., and K. Taşdemir, 2011. Identification of hazelnut fields
using spectral and Gabor textural features,
ISPRS Journal of
Photogrammetry and Remote Sensing
, 66(5):652–661.
Sheng, G., W. Yang, T. Xu, and H. Sun, 2012. High-resolution satellite
scene classification using a sparse coding based multiple
feature combination,
International Journal of Remote Sensing
,
33(8):2395–2412.
Sivic, J., and A. Zisserman, 2003. Video Google: A text retrieval
approach to object matching in videos,
Proceedings of the Ninth
IEEE International Conference on Computer Vision
, 13–16
October 2003, pp. 1470–1477, Vol. 1472.
Susaki, J., M. Kajimoto, and M. Kishimoto, 2014. Urban density
mapping of global megacities from polarimetric SAR images,
Remote Sensing of Environment
, 155:334–348.
Wang, L., S. Hao, Q. Wang, and Y. Wang, 2014. Semi-supervised
classification for hyperspectral imagery based on spatial-spectral
Label Propagation,
ISPRS Journal of Photogrammetry and
Remote Sensing
, 97:123–137.
Vieira, M.A., A.R. Formaggio, C.D. Rennó, C. Atzberger, D.A. Aguiar,
and M.P. Mello, 2012. Object based image analysis and data
mining applied to a remotely sensed Landsat time-series to map
sugarcane over large areas,
Remote Sensing of Environment
,
123:553–562.
Zhu, J., Y. Su, Q. Guo, and T.C. Harmon, 2017. Unsupervised
object-based differencing for land-cover change detection,
Photogrammetric Engineering & Remote Sensing
, 83(3):225–236.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
November 2018
731