XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

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:: CLASSIFICATION OF ACTIVE MICROWAVE AND PASSIVE OPTICAL DATA BASED ON BAYESIAN THEORY AND MRF F. Yu, H. T. Li, Y. S. Han, H. Y. Gu

Document type:: Multivolume work

Structure type:: Chapter

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 other fallow : ; buildin classes corn garden woodland meadow sand mountain saline desert water corps land a 538 321 Training samples 5 1615 495 679 1642 1483 7 4810 4690 3612 587 805 lidati 280 224 ha 1056 2295: 335 1180 — 1176 3202 2146 2484 327 479 samples 4 6 882 546 sum s 2671 724 1014 2822 2659 3 8012 6836 | 6096 914 1284 Table. 1 Training and Validation Samples other fallow - : buildin classes corn garden woodland meadow sand mountain saline desert water corps land g Classification with : 86.2% 58.9% 62.1% 60.3% 83.1% 79.2% 85.4% 73.1% 77.7% 83.6% 872% 91.2% Single TM Classification by 95290 732% 782% 68.8% 91.2% 91.7% 932% 88.9% 92.7% 88.5% 96.8% 98.6% presented method Table. 2 Statistical Errors for the Two Algorithms 4. CONCLUSION A new classification model for active and passive remote sensing data is developed in this paper. In the model, a classifier based on the Bayesian theory and MRF is set up, ASAR in VV, VH polarization and 7 bands of TM are taken as the input of the classifier. The validation by field measurements shows that: 1) The classification model based on Bayesian and MRF in this paper not only need not to assume the conditional probability density function in advance, but also joining the spatial correlation of the class labels, the model can get a better classification accuracy of 89.4%. 2) Comparing with the Classification with single TM, the total precision of classification by active and passive remote sensing increase 11.5%, it shows the integration of TM and ASAR data can increase the information of the surface objects, make them easier to distinguish, and finally reach a better classification precision. The study area is a typical ‘oasis-desert’ dual ecological environment in the paper, and terrain of the oasis is relatively flat. These are conductive to identify and classify the objects in ASAR image. But when the study area is selected a densely populated plains or urban areas, the accuracy of classification by active and passive remote sensing data needs to be further verified. REFERENCES [1] Jia, Y., Li, D. R., 1995. Multisource classification of remotely sensed data based on Bayesian data fusion method, Journal of Wuhan Technical University of surveying and Mapping, 22(3), pp. 248-251. [2] Solberg, A. H., 1994. Multisource Classification of Remotely Sensed Data: Fusion of Landsat TM and SAR Images. IEEE Trans. Geosci. Remote Sens., 32(1), pp. 766- 778. [3] Storvik, G., Roger, F., Solberg, A. H., 2005. A Bayesian approach to classification of multi-resolution remote sensing data. /EEE Trans. Geosci. Remote Sens., 43(3), pp. 539-547. [4] Chellappa, R., Chatterjee, S., 1985. Classification of Textures Using Gaussian Markov Random Fields. IEEE Trans. Acous. Speech. Signal Process., 33(2), pp. 959— 963. [5] Chellappa, R., Hu, T., 1983. On two-dimensional Markov spectral estimation. IEEE Trans. Acous. Speech. Signal Process, 31(4), pp. 836-841. [6] Julian, H., 1986. On the statistical analysis of dirty pictures. J. Roy. Statist. Soc, 48(1), pp. 259-302. [7] Yonhong, J., Philip, H., 1996. Bayesian Contextual Classification Based on Modified M-Estimates and Markov Random Fields. IEEE Trans. Geosci. Remote Sens, 34(1), pp. 67-75. [8] Geman, S., 1984. Markov random field image models and their applications to computer vision. /EEE Trans. Pattern Anal. Mach intell., 26(2), pp. 721-743, ACKNOWLEDGMENT This work is supported by the National Science Foundation for Young Scientists of China (Grant number: 41101321) and the Key Projects in the National Science & Technology Pillar Program (2009BAG18B01and 2012BAH28B03).

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