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

ons G) sity ing cult the fier the sity )6), of sity ^M) lass to lass the (6) «ov (7) we (8) (9) where ó,- Dirac-delta function, if k-0, ój-1; kZ0, 0,0. c7 (c|c€ C, s€ c). Therefore, from (5) and (7) we know: Ws) » argmin Y (-In p(X | w(s))+ BY (1-6, (5,)] w(s) ses ceC (10) 3. EXPERIMENTS 3.1. Study Area and Database The measures reports in this study are conducted during the Watershed Airborne Telemetry Experiment. The study area locates in Grass Station of Lanzhou University in Zhang Ye district, Gansu province. Its geographical coordinates are 39.25043?N, 100.005871?E, the altitude is 1385 meters. Land use mainly consists of country, bare salinization land and irrigative agricultural fields. The field experiment was conducted from June to July in 2008, at which time the crops were corn, clove, barley and other crops. Satellites over the study area provided TM and ASAR data on 7 July 2008 and 11 July 2008, respectively. ASAR (Advanced Synthetic Aperture Radar) is a synthetic aperture radar carried by the ENVISAT-1 satellite and operates in the C- band (central wavelength 5.63 cm), with multi-polarization, seven observation angles and five operating modes. In this study, we chose to use the ASAR data, and the operating mode was Alternation Polarization corresponding to two kinds of polarization (VV and VH) and high space resolution (12.5x12.5 m per pixel). Figure 1(a) and 1(b) illustrate the false color composite image composed by TM3, 4, 5 and ASAR image in VV polarization. T ss fais E: i d (a) The false color composite image (b) ASAR image in VV polarization Figure 1. The images used in the paper When the initial class labels and conditional probability density function of the multi-source remote sensing data are determined by MLMM, formulation (10) is used to perform the local minimization at each pixel in a specified order and get the updated category. If changes occur then repeat estimating. The iteration continues until no more updates occur for all the pixels inside the lattice, then, the classification completes. Comparing with the conventional Markov model for iterative classification, our method needn't to assume the conditional probability density function in advance; With joining the spatial 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 correlation of the class labels, our method also can get a better classification accuracy than the ordinary maximum likelihood classification model. Moreover, the classification is achieved through the iterative process, which takes into account the characteristics of pixel attributes. 3.2. Classification experiment VV, VH polarization of ASAR and all the TM bands are taken as the input of the classifier, and the multisource images are resampled to 30m*30m and geometrically corrected. The study area is separated to 12 classes, which are corn, other corps, garden, woodland, meadow, fallow land, sand, mountain, saline, desert, building, and water. The training samples and validation samples are shown in Table 1. When the training samples and validation samples are selected, we use the method in Section 2 to classify the TM and ASAR images, the results are shown in Figure 2, In which, the basic form of oasis is similar with the Figure 1(a), which is consistent with the dual ecological environment of western semiarid regions, “oasis accompanies with water, and desert accompanies with no water”. Fhe Classification of Heihe Bivel By ASAR and TM dhesest taciding water Figure 2. The Classification map of study area i} ; LM tomes 3.3. Validation To verify the necessity of coupling optical radar data for classification, the output of the classification of ASAR and TM in Section 3 were compared with the classification only by TM, all using Bayesian and MRF classifier. Table 2 presents the statistical errors among the three algorithms. In Table 2, the accuracy of each type of classification with single TM is lower, and reaches a total precision of 77.9%. When the ASAR dual polarization is jointed, the total precision increases to 89.496. The reason may be that the ASAR information can increase the surface characteristics and make them easy to distinguish, for example, corn, other corps, garden and woodland are similar in spectrum, and we can identify them by their various structural features revealed by their ASAR backscattering coefficients and finally obtain a better accuracy.

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