메뉴 건너뛰기




Volumn 2, Issue 3W1, 2013, Pages 35-40

BEYOND HAND-CRAFTED FEATURES in REMOTE SENSING

Author keywords

classification; feature extraction; land cover; pattern recognition

Indexed keywords


EID: 84976377657     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprsannals-II-3-W1-35-2013     Document Type: Conference Paper
Times cited : (15)

References (36)
  • 1
    • 43049174575 scopus 로고    scopus 로고
    • Speededup robust features (SURF)
    • Bay, H., Ess, A., Tuytelaars, T. and van Gool, L., 2008. Speededup robust features (SURF). CVIU 110(3), pp. 346-359.
    • (2008) CVIU , vol.110 , Issue.3 , pp. 346-359
    • Bay, H.1    Ess, A.2    Tuytelaars, T.3    Van Gool, L.4
  • 3
    • 78049290930 scopus 로고    scopus 로고
    • A novel technique for subpixel image classification based on support vector machine
    • Bovolo, F., Bruzzone, L. and Carlin, L., 2010. A novel technique for subpixel image classification based on support vector machine. IEEE Transactions on Image Processing 19(11), pp. 2983-2999.
    • (2010) IEEE Transactions on Image Processing , vol.19 , Issue.11 , pp. 2983-2999
    • Bovolo, F.1    Bruzzone, L.2    Carlin, L.3
  • 4
    • 0036821351 scopus 로고    scopus 로고
    • Multiple classifiers applied to multisource remote sensing data
    • Briem, G., Benediktsson, J. and Sveinsson, J., 2002. Multiple Classifiers Applied to Multisource Remote Sensing Data. IEEE TGRS 40(10), pp. 2291-2299.
    • (2002) IEEE TGRS , vol.40 , Issue.10 , pp. 2291-2299
    • Briem, G.1    Benediktsson, J.2    Sveinsson, J.3
  • 5
    • 79958822032 scopus 로고    scopus 로고
    • The dgpf-test on digital airborne camera evaluation overview and test design
    • Cramer, M., 2010. The dgpf-test on digital airborne camera evaluation overview and test design. Photogrammetrie-Fernerkundung-Geoinformation 2, pp. 73-82.
    • (2010) Photogrammetrie-Fernerkundung-Geoinformation , vol.2 , pp. 73-82
    • Cramer, M.1
  • 7
    • 84907056877 scopus 로고    scopus 로고
    • Evaluation of texture energies for classification of facade images
    • Drauschke, M. and Mayer, H., 2010. Evaluation of texture energies for classification of facade images. In: IAPRS, Vol. 38.
    • (2010) IAPRS , vol.38
    • Drauschke, M.1    Mayer, H.2
  • 8
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund, Y. and Schapire, R., 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), pp. 119-139.
    • (1997) Journal of Computer and System Sciences , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.2
  • 9
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting
    • Friedman, J., Hastie, T. and Tibshirani, R., 2000. Additive logistic regression: a statistical view of boosting. Annals of Statistics 38(2), pp. 337-374.
    • (2000) Annals of Statistics , vol.38 , Issue.2 , pp. 337-374
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 10
    • 85048924309 scopus 로고    scopus 로고
    • Texture segmentation by multiscale aggregation of filter responses and shape elements
    • Galun, M., Sharon, E., Basri, R. and Brandt, A., 2003. Texture segmentation by multiscale aggregation of filter responses and shape elements. In: CVPR.
    • (2003) CVPR
    • Galun, M.1    Sharon, E.2    Basri, R.3    Brandt, A.4
  • 12
    • 0030288153 scopus 로고    scopus 로고
    • Texture modeling by multiple pairwise pixel interactions
    • Gimel'farb, G., 1996. Texture Modeling by Multiple Pairwise Pixel Interactions. PAMI 18(11), pp. 1110-1114.
    • (1996) PAMI , vol.18 , Issue.11 , pp. 1110-1114
    • Gimel'Farb, G.1
  • 15
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G. and Salakhutdinov, R., 2006. Reducing the Dimensionality of Data with Neural Networks. Science 313(5786), pp. 504-507.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.1    Salakhutdinov, R.2
  • 16
    • 84898427819 scopus 로고    scopus 로고
    • Feature sets and dimensionality reduction for visual object detection
    • Hussain, S. and Triggs, B., 2010. Feature Sets and Dimensionality Reduction for Visual Object Detection. In: BMVC.
    • (2010) BMVC
    • Hussain, S.1    Triggs, B.2
  • 17
  • 18
    • 0035358496 scopus 로고    scopus 로고
    • Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
    • Leung, T. and Malik, J., 2001. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons. IJCV 43(1), pp. 29-44.
    • (2001) IJCV , vol.43 , Issue.1 , pp. 29-44
    • Leung, T.1    Malik, J.2
  • 19
    • 3042525106 scopus 로고    scopus 로고
    • Learning to detect natural image boundaries using local brightness, color, and texture cues
    • Martin, D., Fowlkes, C. and Malik, J., 2004. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE TPAMI 26(5), pp. 530-549.
    • (2004) IEEE TPAMI , vol.26 , Issue.5 , pp. 530-549
    • Martin, D.1    Fowlkes, C.2    Malik, J.3
  • 20
    • 84055175817 scopus 로고    scopus 로고
    • Learning to detect roads in high-resolution aerial images
    • Mnih, V. and Hinton, G. E., 2010. Learning to detect roads in high-resolution aerial images. In: ECCV.
    • (2010) ECCV
    • Mnih, V.1    Hinton, G.E.2
  • 21
    • 84867136367 scopus 로고    scopus 로고
    • Learning to label aerial images from noisy data
    • Mnih, V. and Hinton, G. E., 2012. Learning to label aerial images from noisy data. In: ICML.
    • (2012) ICML
    • Mnih, V.1    Hinton, G.E.2
  • 22
    • 13344278660 scopus 로고    scopus 로고
    • Random forest classifier for remote sensing classification
    • Pal, M., 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing 26(1), pp. 217-222.
    • (2005) International Journal of Remote Sensing , vol.26 , Issue.1 , pp. 217-222
    • Pal, M.1
  • 23
    • 34948870900 scopus 로고    scopus 로고
    • Unsupervised learning of invariant feature hierarchies with applications to object recognition
    • Ranzato, M., Huang, F., Boureau, Y. and LeCun, Y., 2007. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. In: CVPR.
    • (2007) CVPR
    • Ranzato, M.1    Huang, F.2    Boureau, Y.3    LeCun, Y.4
  • 24
    • 84856954109 scopus 로고    scopus 로고
    • Endmember Extraction Using a Combination of Orthogonal Projection and Genetic Algorithm
    • Rezaei, Y., Mobasheri, M., Zoej, M. V. and Schaepman, M., 2012. Endmember Extraction Using a Combination of Orthogonal Projection and Genetic Algorithm. GRSL 9(2), pp. 161-165.
    • (2012) GRSL , vol.9 , Issue.2 , pp. 161-165
    • Rezaei, Y.1    Mobasheri, M.2    Zoej, M.V.3    Schaepman, M.4
  • 26
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • Schapire, R. and Singer, Y., 1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), pp. 297-336.
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 297-336
    • Schapire, R.1    Singer, Y.2
  • 27
    • 84869491579 scopus 로고    scopus 로고
    • An overview and comparison of smooth labeling methods for land-cover classification
    • Schindler, K., 2012. An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification. IEEE TGRS 50(11), pp. 4534-4545.
    • (2012) IEEE TGRS , vol.50 , Issue.11 , pp. 4534-4545
    • Schindler, K.1
  • 28
    • 0035680897 scopus 로고    scopus 로고
    • Constructing models for content-based image retrieval
    • Schmid, C., 2001. Constructing Models for Content-based Image Retrieval. In: CVPR.
    • (2001) CVPR
    • Schmid, C.1
  • 29
    • 85015879350 scopus 로고    scopus 로고
    • Human detection using partial least squares analysis
    • Schwartz, W., Kembhavi, A., Harwood, D. and Davis, L., 2009. Human detection using partial least squares analysis. In: ICCV.
    • (2009) ICCV
    • Schwartz, W.1    Kembhavi, A.2    Harwood, D.3    Davis, L.4
  • 31
    • 85048921970 scopus 로고    scopus 로고
    • An evaluation of feature learning methods for high resolution image classification
    • Tokarczyk, P., Montoya, J. and Schindler, K., 2012. An evaluation of feature learning methods for high resolution image classification. ISPRS Annals.
    • (2012) ISPRS Annals
    • Tokarczyk, P.1    Montoya, J.2    Schindler, K.3
  • 32
    • 34548435161 scopus 로고    scopus 로고
    • Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium
    • van Coillie, F., Verbeke, L. and Wulf, R. D., 2007. Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium. Remote Sensing of Environment 110, pp. 476-487.
    • (2007) Remote Sensing of Environment , vol.110 , pp. 476-487
    • Van Coillie, F.1    Verbeke, L.2    Wulf, R.D.3
  • 33
    • 0035680116 scopus 로고    scopus 로고
    • Rapid object detection using a boosted cascade of simple features
    • Viola, P. and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: CVPR.
    • (2001) CVPR
    • Viola, P.1    Jones, M.2
  • 34
    • 36349007145 scopus 로고    scopus 로고
    • Fusion of support vector machines for classification of multisensor data
    • Waske, B. and Benediktsson, J., 2007. Fusion of support vector machines for classification of multisensor data. IEEE TGRS 45(12), pp. 3858-3866.
    • (2007) IEEE TGRS , vol.45 , Issue.12 , pp. 3858-3866
    • Waske, B.1    Benediktsson, J.2
  • 35
    • 33745913325 scopus 로고    scopus 로고
    • Object categorization by learned universal visual dictionary
    • Winn, A., Criminisi, A. and Minka, T., 2005. Object categorization by learned universal visual dictionary. In: ICCV.
    • (2005) ICCV
    • Winn, A.1    Criminisi, A.2    Minka, T.3
  • 36
    • 0000806445 scopus 로고    scopus 로고
    • Minimax entropy principle and its application to texture modeling
    • Zhu, S., Wu, Y. and Mumford, D., 1997. Minimax Entropy Principle and Its Application to Texture Modeling. Neural Computation 9(8), pp. 1627-1660.
    • (1997) Neural Computation , vol.9 , Issue.8 , pp. 1627-1660
    • Zhu, S.1    Wu, Y.2    Mumford, D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.