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

for its $ in the Ires iral 00. ion acy Ces tial ata. the ous ote lent pp. to tial A lass for ints 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 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 Chinese Academy of Surveying and Mapping, Beijing 100830, P. R. China — (yufan, Ihtao, hys han, guhy)(g)casm.ac.cn Commission VII, WG VII/4 KEY WORDS: Active and passive remote sensing, Classification, Bayesian theory, MRF, ASAR, TM. ABSTRACT A classifier based on Bayesian theory and Markov random field (MRF) is presented to classify the active microwave and passive optical remote sensing data, which have demonstrated their respective advantages in inversion of surface soil moisture content. In the method, the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. And the model is validated for the necessities of integration of TM and ASAR, it shows that, the total precision of classification in this paper is 89.4%. Comparing with the classification with single TM, the accuracy increase 11.5%, illustrating that synthesis of active and passive optical remote sensing data is efficient and potential in classification. 1. INTRODUCTION A range of remotely sensed data from sensors differing in terms of their spectral, spatial, and temporal resolution is now widely available. Given the large size of such multisource data sets, the immediate problem is how to choose and apply a suitable classification algorithm in order to achieve a level of accuracy that is acceptable for the given application. Optic and microwave remote sensing are two common methods to obtain the land surface information. The optic remote sensing data, with rich spectral information, represents the surface reflective spectral or the emission spectral; Microwave remote sensing has the characteristics of strong penetration, and it is the general information of vegetation coverage, surface roughness, dielectric constant, structure and so on. When the optic images are helpless with the problems of ‘foreign objects with the same spectrum’ and ‘identical objects with the different spectrum’ in the earth observation, the microwave images can distinguish the objects by its surface roughness, structure, shape, water content and so on. Therefore, the integration of optic and microwave remote sensing data can gain the features of objects in different aspects, and do well in the classification or feature extraction. Currently, integration of optical and microwave remote sensing in classification is attracting increasing attention, Reference (jia et al, 1995) used modified Bayesian Network to classify the Landsat TM and Aircraft SAR images, and found the precision of the classification by fusion TM and ASR are 20% higher than the single TM. The decision level fusion of TM and SAR images was applied to classification (Solberg, 1994), and further improved by adding the Markov random field; Storvik (Storvik, 2005) proposed Bayesian network to classify the multisource remote sensing with different spatial * . . . . . * Corresponding author, Ph. D, majors in classification with active microwave and passive optical remote sensing data. resolution and get an accuracy of 88.7%. However, the above reference can not handle the SAR image speckle noise, and discuss less of the extraction of the multi-feature of SAR, so they didn't set up the appropriate conditional probability density model. Consequently, we have developed a new classification model for multisource data based on the Markov Random Field (MRF) and Bayesian theory. In the model, a Bayesian classifier based on MRF is developed, the VV, VH polarization of ASAR and all the TM bands are taken as the input of the Bayesian classifier to get the class label of each pixels of the mutilsource images. At last, the model is validated by the field measurements. 2. CLASSIFIER BASED ON BAYESIAN THEORY AND MRF Bayesian statistical theory has been widely used as a theoretically robust foundation for the classification of remotely sensed data. The matter of multi-source remote sensing imagery is : suppose multivariate image X is composed of N- dimensional pixels where X,,, denotes the eigenvector of X, k — 1,2, ..., N, presents the N dimensions, and s = (i, j) denotes the coordinate on image X. w denotes the field which contains the classification of each pixel in X; points in w can take values in the set {1, 2, . . . , L}, where L is the number of classes. The multivariate image X is then classified by finding a field of class labels Wap such that:

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