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:: LAND COVER CLASSIFICATION OF MULTI-SENSOR IMAGES BY DECISION FUSION USING WEIGHTS OF EVIDENCE MODEL Peijun Li and Bengin Song

Document type:: Multivolume work

Structure type:: Chapter

From expressions (2) and (3), we can obtain: log, O(D | E, ) = log, O(D)+ Ww, log, O(D | E,) - log, O(D)- W; (4) Similarly, if more than two evidence maps are used, they can be added by assuming that all the evidence maps are also conditionally independent with respect to the occurrence of a class. Therefore, we can obtain, log, O(D | E! E: E!) - log, O(D)- Y W/ e ne where the superscript & refers to the presence or absence of the evidence, respectively. As mentioned previously, the WOE assumes that all the evidence patterns are conditionally independent. However, in most applications, = evidence patterns are conditionally dependent. Thus, posterior probability estimates by directly using WOE are likely to be biased upwards when this assumption is violated. In this study, we adopted a modified WOE model, which is called the conditional dependence adjusted weights of evidence (CDAWE) (Deng, 2009), to correct the bias using the correlation structure of the evidence patterns. 2.2 Fusion of multi-sensor data for land cover classification In this study, the Support Vector Machine (SVM) was first applied on different data to produce land cover classification results. The SVM classifier is a recently developed statistical learning method, which has been widely used in remote sensing image classification and showed very good performance (e.g., Gualtieri and Cromp, 1998; Huang et al., 2002; Melgani and Bruzzone, 2004). The obtained urban classification results were then fused by weights of evidence model. The estimation of conditional probability for each class is crucially important, since it directly related to the weight of each evidential map. In this study, the intermediate classification results derived from the SVM classification, i.e. posterior probability of classification, either from classification rule image as in ENVI or using the method proposed by Platt (2000) were used as the initial conditional probability of each class. Since classification results from different data show different accuracies (i.e. class reliability), final conditional probability for each class is obtained by combining conditional probability from the initial classification and class reliability (i.e. class accuracy). Since the conventional weights of evidence method is defined for one- class extraction (e.g. mineral occurrence), in order to extend it to multi-class classification, the weights of evidence method was first used in classification to produce a posterior probability for each class, and then the class for each pixel is estimated by taking the most probable class of the posterior distribution (i.e. with highest posterior probability). 3. EXPERIMENTAL RESULTS AND DISCUSSION The proposed method was evaluated and validated in land cover classification using two examples. The first example includes Landsat TM and multitemporal ENVISAT ASAR data, covering Beijing urban-suburban area, China. The second example includes Landsat ETM+ and SPOT 5 multispectral images of Hengshui area of Hebei Province, China. Landsat 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 TM/ETM+ and ENVISAT ASAR data have a 30m spatial resolution, whereas the SPOT 5 data have a 10 m spatial resolution (band 4 has 20 m spatial resolution). Results showed that the combination of multi-sensor data using weights of evidence model produced higher classification accuracy than the use of single data alone. For example, for Beijing area, combined classification based on WOE produced 41.72% and 3.22% of increase in Kappa coefficient, compared with those from SAR and TM images, respectively (Table 1). For Hengshui area, combined classification based on WOE produced 7.08% and 6.22% of increase in Kappa coefficient, compared with those from ETM+ and SPOT 5 images, respectively (Table 2). It seems that when two individual classification results show comparable accuracy, the increase in classification accuracy (both overall accuracy and Kappa coefficient) produced by WOE based decision fusion is more significant (i.e. in Table 2). Figures 1 and 2 show portions of classifications using difference data combinations for two examples. From these figures, WOE effectively fused the classification results from different results. For example, salt-pepper appearance was significantly reduced. 4. CONCLUSION A decision fusion method based on the weights of evidence model was proposed in this study and evaluated in land cover classification using two different es. Results showed that the combination of different data using weights of evidence model in land cover classification produced significantly higher accuracy (both overall accuracy and Kappa) than the use of single source of data alone. In conclusion, the weights of evidence model provides an effective decision fusion method for improved land cover classification using multi-sensor data. 5. REFERENCES Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1988. Integration of geological datasets for gold exploration in Nova Scotia. Photogram. Remote Sens., v. 54, no. 11, p. 1585-1592. Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1989. Weights of evidence modelling: a new approach to mapping mineral potential, in Agterberg, F. P., and Bonham-Carter, G. F., eds., Statistical Applications in the Earth Sciences: Geological Survey Canada paper 9-9, p. 171—183. Deng, M., 2009. A Conditional Dependence Adjusted Weights of Evidence Model. Natural Resources Research, 18(4), 249- 258. Good, I. J., 1985. Statistical evidence. In Encyclopedia of statistical Sciences, New York, Wiley, pp., 651-656. Gualtieri, J.A., and R.F. Cromp, 1998. Support vector machines for hyperspectral remote sensing classification, Proceedings of SPIE, the International Society for Optical Engineering, 3584: 221-232. Huang, C., L.S. Davis, and J.R.G. Townshend, 2002. An assessment of support vector machines for land cover classification, /nternational Journal of Remote Sensing, 23(4):725—749.

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