Abstract
This study proposes a new method for land use and land cover (LULC) change detection using RADARSAT-2 polarimetric SAR (PolSAR) images. The proposed method combines change vector analysis (CVA) and post-classification analysis (PCC) to detect LULC changes using RADARSAT-2 PolSAR images based on object-oriented image analysis. A hierarchical segmentation was implemented on two RADARSAT-2 PolSAR images acquired at different times to delineate image objects. CVA was applied to the coherency matrix of PolSAR images to identify changed objects, and then PCC was used to determine the type of changes. The classification of the RADARSAT-2 images is based on the integration of polarimetric decomposition, object-oriented image analysis, decision tree algorithms, and support vector machines (SVMs). In comparison with the PCC that is based on the Wishart supervised classification, the proposed method improves the overall error rate for change detection and the overall accuracy for change type determination by 25.15 and 6.59 % respectively. The results show that the proposed method can achieve much higher accuracy for LULC change detection using RADARSAT-2 PolSAR images than the PCC that is based on the Wishart supervised classification.
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Alberga V (2007) A study of land cover classification using polarimetric SAR parameters. Int J Remote Sens 28:3851–3870
Barnes RM (1988) Roll-invariant decompositions for the polarization covariance matrix. Polarimetry technology workshop, Redstone Arsenal, AL
Bovolo F, Bruzzone L (2007) A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Trans Geosci Remote Sens 45:218–236
Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Rojo-Alvarez JL, Martinez-Ramon M (2008) Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans Geosci and Remote Sens 46:1822–1835
Chen KS, Huang WP, Tsay DH, Amar F (1996) Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network. IEEE Trans Geosci Remote Sens 34:814–820
Cloude SR, Pottier E (1997) An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans Geosci Remote Sens 35:68–78
Congalton R, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, Boca Raton
Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:1565–1596
Howarth PJ, Wickware GM (1981) Procedures for change detection using Landsat digital data. Int J Remote Sens 2:277–291
Lambin EF, Strahler AH (1994) Change-vector analysis in multitemporal space–a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sens Environ 48:231–244
Lee JS, Grunes MR, Kwok R (1994) Classification of multi-look polarimetric SAR imagery-based on complex Wishart distribution. Int J Remote Sens 15:2299–2311
Lee JS, Grunes MR, Ainsworth TL, Du LJ, Schuler DL, Cloude SR (1999) Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Trans Geosci Remote Sens 37:2249–2258
Lee JS, Wen JH, Ainsworth TL, Chen KS, Chen AJ (2009) Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans Geosci Remote Sens 47:202–213
Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2407
Malila WA (1980) Change vector analysis: an approach for detecting forest changes with Landsat. In: Lafayette W (ed) Proceedings of remotely sensed data symposium
Petit CC, Lambin EF (2001) Integration of multi-source remote sensing data for land cover change detection. Int J Geogr Inf Sci 15:785–803
Qi Z, Yeh AG-O, Li X, Lin Z (2012) A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data. Remote Sens Environ 118:21–39
Rignot E, Chellappa R, Dubois P (1992) Unsupervised segmentation of polarimetric SAR data using the covariance-matrix. IEEE Trans Geosci Remote Sens 30:697–705
Shen G, Guo H, Liao J (2007) Change vector analysis method for inundation change detection using multi-temporal multi-polarized SAR images. In: Proceedings of SPIE, Wuhan, China
Shimoni M, Borghys D, Heremans R, Perneel C, Acheroy M (2009) Fusion of PolSAR and PolInSAR data for land cover classification. Int J Appl Earth Obs Geoinf 11:169–180
Silapaswan CS, Verbyla DL, McGuire AD (2001) Land cover change on the Seward Peninsula: the use of remote sensing to evaluate the potential influences of climate warming on historical vegetation dynamics. Can J Remote Sens 27:542–554
Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10:989–1003
Stow DA, Tinney LR, Estes JE (1980) Deriving land use/land cover change statistics from Landsat: a study of prime agricultural land. In: Proceedings of the 14th international symposium on remote sensing of environment, Ann Arbor, Michigan
Weismiller RA, Kristof SJ, Scholz DK, Anuta PE, Momin SA (1977) Change detection in coastal zone environments. Photogramm Eng Remote Sens 43:1533–1539
Acknowledgments
This work was supported by the Science and Operational Applications Research for RADARSAT-2 Program (SOAR 2762). The authors would like to thank the Canadian Space Agency (CSA) and the MDA GEOSPATIAL SERVICES INC. for providing the RADARSAT-2 data.
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Qi, Z., Yeh, A.GO. (2013). Integrating Change Vector Analysis, Post-Classification Comparison, and Object-Oriented Image Analysis for Land Use and Land Cover Change Detection Using RADARSAT-2 Polarimetric SAR Images. In: Timpf, S., Laube, P. (eds) Advances in Spatial Data Handling. Advances in Geographic Information Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32316-4_8
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DOI: https://doi.org/10.1007/978-3-642-32316-4_8
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