XXII ISPRS Congress 2012: Technical Commission VII

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Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: POST-CLASSIFICATION APPROACH BASED ON GEOSTATISTICS TO REMOTE SENSING IMAGES : SPECTRAL AND SPATIAL INFORMATION FUSION N. Yao, J. X. Zhang, Z. J. Lin, C. F. Ren

Document type:: Multivolume work

Structure type:: Chapter

in Figure 3(b), the increased posterior probability pertaining to the right type is still not predominant to exceed the decreased probability pertaining to the confusing types. However, the effect that the SK method helps to improve the predicted probability of the right land cover type is still traceable. In Figure 3(d) the SK method fails to further improve the originally prevailing posterior probabilities pertaining to the right type, but instead meets the exact reverse. It may come down to two reasons: (1) the input vectors are prone to confusion which results in the slight advantage in the posterior probability of the right type; (2) the training samples in a local domain are sparse and unevenly distributed which results in an unfaithful modelling of residual variation and a naive spatial interpolation. That is, the less accurate residual variogram models would easily reverse the slight advantage. However, samples of this kind in Figure 3(b) only occupy 1.1 percent of the total samples. On the contrary, samples of the kind in Figure 3(a), which are poorly classified but correctly revised, occupy 58 percent. Moreover, compared to the producer's and user's accuracy acquired by the SVM classification, as are listed in Table 2, the corresponding accuracy fluctuations after the application of the SK method may not equivalent to mean that the kriging method is particularly suitable to some certain land cover types. In order to further testify the efficiency of the kriging paradigm, another TM image including 17 land cover types is adopted. A total of ten variables were available: Landsat TM channels 1-5, 7, modified normalized difference vegetation index (MNDVI), scaled elevation, slope in degrees, and a combined slope-aspect variable. Further detail of this data set is documented in Zhang and Goodchild (2007). The estimated Arif index manifests a corresponding highest classification accuracy of 72.2296. The overall accuracy and kappa coefficient achieved by generalized linear model (abbreviated to GLM) are 65.55% and 0.62, respectively. After residual corrections, the former is improved to 75.45% and the latter is increased to 0.73. It is interesting to notice that the revised accuracy of 75.45% is larger than the estimated potential highest accuracy of 72.22%. The is that the potential highest is just estimated by the input vectors without considering the introduced spatial information during the post- classification corrections. 4. CONCLUSION The proposed two kriging methods are independent of the specific classifiers initially adopted for image classification. Hence, these methods may be treated as post-classification approaches. They aim at compensating part of the information consumed by classifiers due to the insufficient learning process. Moreover, these methods are independent of the land cover types, although the improvements vary in the producer’s and user’s accuracies of different land covers. The factors, including the sampling methods, sample sizes, and sample distributions, need to be further investigated, for they are close related to the spatial information of the training samples. REFERENCE Atkinson P. M., Lewis P., 2000. Geostatistical classification for remote sensing: an introduction. Computers and Geosciences, 26, pp. 361-371. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia Arif M., Afsar F. A., Akram M. U., et al., 2009. Arif index for predicting the classification accuracy of features and its application in heart beat classification problem. In: Advances in Knowledge Discovery and Data Mining: proceedings of the 13th Pacific-Asia Conference, Thailand. Battiti R., 1995. Using mutual information for selecting features in supervised neural net learning. /EEE Transactions on Neural Networks, 5(4), pp. 537-551. Food and Agriculture Organization of the United Nations, 2000. Land Cover Classification System (LCCS): Classification Concepts and User Manual. http://www.fao.org/docrep/003/x0596e/x0596e00.htm(2011) Foody G. M., 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, pp.185-201. Goovaerts P, 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, pp. 125-233. Goovaerts P., 2002. Geostatistical incorporation of spatial coordinates into supervised classification of hyperspectral data. Geographical systems, 4, pp. 9-111. Homer C., Dewitz J., Fry J., et al, 2007. Completion of the 2001 National Land Cover Database for the Conterminous United States. Photogrammetric Engineering and Remote Sensing, 73(4), pp. 337-341. Homer C., Huang C. Q., Yang L. M., et al., 2004. Development of a 2001 National Land-Cover Database for the United States. Photogrammetric Engineering and Remote Sensing, 70(7), pp. 829—840. Meyer D., 2009. Support Vector Machines-The Interface to Libsvm in Package e1071. http://cran.r-project.org/web/packages/e107 1/vignettes/ svmdoc.pdf (2011) Zhang J. X., 2009. Scale, Uncertainty and Fusion of Spatial Information. Wuhan University Press, Wuhan, pp. 221-234. Zhang J. X., Goodchild, M. F., Steele, B. M. and et al., 2007. A discriminant space-based framework for scalable area-class mapping. In: Proceedings of SPIE-The International Society for Optical Engineering, Nanjing, China, Vol. 6751. ACKNOWLEDGEMENT The research is partially supported by “973 Program” grants (No. 41071286) and (No. 41171346 ).

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