A segment-based approach to classify agricultural lands by using multi-temporal optical and microwave data

References

• ASAR Product Handbook, 2009 http://envisat.esa.int/handbooks/asar (http://envisat.esa.int/handbooks/asar ) (Accessed: 15 November 2009 ).
   Google Scholar
• Ban , Y. 2003 . Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops . Canadian Journal of Remote Sensing , 29 : 518 – 526 .
   Web of Science ®Google Scholar
• Ban , Y. , Hu , H. and Rangel , I.M. 2010 . Fusion of QuickBird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach . International Journal of Remote Sensing , 31 : 1391 – 1410 .
   Web of Science ®Google Scholar
• Blaes , X. , Holecz , F. , Van Leeuwen , H.J.C. and Defourny , P. 2007 . Regional crop monitoring and discrimination based on simulated ENVISAT ASAR wide swath mode images . International Journal of Remote Sensing , 28 : 371 – 393 .
   Web of Science ®Google Scholar
• Blaes , X. , Vanhalle , L. and Defourny , P. 2005 . Efficiency of crop identification based on optical and SAR image time series . Remote Sensing of Environment , 96 : 352 – 365 .
   Web of Science ®Google Scholar
• Blaschke , T. 2010 . Object based image analysis for remote sensing . ISPRS Journal of Photogrammetry and Remote Sensing , 65 : 2 – 16 .
   Web of Science ®Google Scholar
• Chabrier , S. , Emile , B. , Rosenberg , C. and Laurent , H. 2006 . Unsupervised performance evaluation of image segmentation . EURASIP Journal on Applied Signal Processing , 2006, 96306 doi: 10.1155/ASP/2006/96306
   PubMedGoogle Scholar
• Chen , D. and Stow , D. 2002 . The effect of training strategies on supervised classification at different spatial resolutions . Photogrammetric Engineering and Remote Sensing , 68 : 1155 – 1161 .
   Web of Science ®Google Scholar
• Chen , Z. , Zhao , Z. , Gong , P. and Zeng , B. 2006 . A new process for the segmentation of high resolution remote sensing imagery . International Journal of Remote Sensing , 27 : 4991 – 5001 .
   Web of Science ®Google Scholar
• Cheng , H.D. , Jiang , X.H. , Sun , Y. and Wang , J. 2001 . Color image segmentation: advances and prospects . Journal of Pattern Recognition , 34 : 2259 – 2281 .
   Web of Science ®Google Scholar
• Cheng , Y. 1995 . Mean shift, mode seeking, and clustering . IEEE Transactions on Pattern Analysis and Machine Intelligence , 17 : 790 – 799 .
   Web of Science ®Google Scholar
• Clinton , N. , Holt , A. , Scarborough , J. , Yan , L.I. and Gong , P. 2010 . Accuracy assessment measures for object-based image segmentation goodness . Photogrammetric Engineering and Remote Sensing , 76 : 289 – 299 .
   Web of Science ®Google Scholar
• Comaniciu , D. and Meer , P. Robust analysis of feature spaces: color image segmentation . IEEE Conference on Computer Vision and Pattern Recognition . June 7–19 1997 . pp. 750 – 755 . San Juan : Rico .
   Google Scholar
• Comaniciu , D. and Meer , P. 2002 . Mean shift: a robust approach toward feature space analysis . IEEE Transactions on Pattern Analysis and Machine Intelligence , 24 : 603 – 619 .
   Web of Science ®Google Scholar
• Corcoran , P. , Winstanley , A. and Mooney , P. 2010 . Segmentation performance evaluation for object-based remotely sensed image analysis . International Journal of Remote Sensing , 31 : 617 – 645 .
   Web of Science ®Google Scholar
• De Wit , A.J.W. and Clevers , J.G.P.W. 2004 . Efficiency and accuracy of per-field classification for operational crop mapping . International Journal of Remote Sensing , 25 : 4091 – 4112 .
   Web of Science ®Google Scholar
• Frost , V.S. , Stiles , J.A. , Shanmugan , K.S. and Holtzman , J.C. 1982 . A model for radar images and its application to adaptive digital filtering of multiplicative noise . IEEE Transactions on Geosciences and Remote Sensing , 4 : 157 – 166 .
   Google Scholar
• Ge , F. , Wang , S. and Liu , T. Image-segmentation evaluation from the perspective of salient object extraction . Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) . June 17–22 2006 . pp. 1146 – 1153 . New York , NY
   Google Scholar
• Gong , P. and Howarth , P.J. 1990 . The use of structural information for improving land-cover classification accuracies at the rural-urban fringe . Photogrammetric Engineering and Remote Sensing , 56 : 67 – 73 .
   Web of Science ®Google Scholar
• Gong , P. and Howarth , P.J. 1992 . Frequency-based contextual classification and grey-level vector reduction for land-use identification . Photogrammetric Engineering and Remote Sensing , 58 : 423 – 437 .
   Web of Science ®Google Scholar
• Gong , P. , Marceau , D. and Howarth , P.J. 1992 . A comparison of spatial feature extraction algorithms for land-use mapping with SPOT HRV data . Remote Sensing of Environment , 40 : 137 – 151 .
   Web of Science ®Google Scholar
• Henderson , F.M. and Lewis , A.J. 1998 . Principles & Applications of Imaging Radar, Manual of Remote Sensing , 3rd , 67 – 68 . New York , NY : John Wiley & Sons .
   Google Scholar
• Jensen , J.R. 2005 . Introductory Digital Image Processing , 3rd , Vol. 175 , 198 – 202 . New York , NY : John Wiley & Sons .
   Google Scholar
• Kuan , D.T. , Sawchuck , A.A. , Strand , T.C. and Chavel , P. 1985 . Adaptive noise smoothing filter for images with signal-dependent noise . IEEE Transactions on Pattern Analysis and Machine Intelligence , 7 : 165 – 177 .
   PubMed Web of Science ®Google Scholar
• Lee , J.S. 1980 . Digital image enhancement and noise filtering by use of local statistics . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2 : 65 – 168 .
   Web of Science ®Google Scholar
• Lee , J.Y. and Warner , T.A. 2006 . Segment based image classification . International Journal of Remote Sensing , 27 : 3403 – 3412 .
   Web of Science ®Google Scholar
• Li , P. and Xiao , X. 2007 . Multispectral image segmentation by a multichannel watershed-based approach . International Journal of Remote Sensing , 28 : 4429 – 4452 .
   Web of Science ®Google Scholar
• Lillesand , T.M. and Kiefer , R.W. 2000 . Remote Sensing and Image Interpretation , New York : John Wiley and Sons .
   Google Scholar
• Lillesand , M. , Kiefer , R.W. and Chipman , J.W. 2004 . Remote Sensing and Image Interpretation , 5th , 638 Hoboken , NJ : Wiley .
   Google Scholar
• Liu , J. and Yang , Y.-H. 1994 . Multi-resolution color image segmentation . IEEE Transactions on Pattern Analysis and Machine Intelligence , 16 : 689 – 700 .
   Web of Science ®Google Scholar
• Liu , L. , Wang , J. , Bao , Y. , Huang , W. , Ma , Z. and Zhao , C. 2006 . Predicting winter wheat condition, grain yield and protein content using multi-temporal Envisat-ASAR and Landsat TM satellite images . International Journal of Remote Sensing , 27 : 737 – 753 .
   Web of Science ®Google Scholar
• Lu , D. and Weng , Q. 2007 . A survey of image classification methods and techniques for improving classification performance . International Journal of Remote Sensing , 28 : 823 – 870 .
   Web of Science ®Google Scholar
• Martin , D. , Fowlkes , C. , Tal , D. and Malik , J. 2004 . Learning to detect natural image boundaries using local brightness, color and texture . IEEE Transactions on Pattern Analysis and Machine Intelligence , 26 : 530 – 549 .
   PubMed Web of Science ®Google Scholar
• Minimax . 2004 . Reference Manual , Calgary : CSI wireless .
   Google Scholar
• Mueller , M. , Segl , K. and Kaufmann , H. 2003 . Extracting characteristic segments in high-resolution panchromatic imagery as basic information for objects-driven image analysis . Canadian Journal of Remote Sensing , 29 : 453 – 457 .
   Web of Science ®Google Scholar
• Nik System, 2008, Kompsat-2 technical notes. http://nik.com.tr/2008/tr/sistem/uydu_goruntuleri/kompsat2.html (http://nik.com.tr/2008/tr/sistem/uydu_goruntuleri/kompsat2.html)
   Google Scholar
• Ozdarici , A. and Akyurek , Z. Evaluating the contribution of image fusion methods into the classification accuracies of agricultural lands . Proceedings of Türkiye Ulusal Fotogrammetri ve Uzaktan Algılama Birliği V. Teknik Sempozyumu (TUFUAB),cd . February 23–25 2009 . Turkey
   Google Scholar
• Ozdarici , A. and Akyurek , Z. A comparison of SAR filtering techniques on agricultural area identification . Proceedings of ASPRS 2010 Annual Conference, cd . April 26–30 . San Diego , CA
   Google Scholar
• Ozdarici Ok , A. and Akyurek , Z. 2011 . Evaluation of image fusion methods on agricultural lands . Journal of Earth Science and Engineering , 1 : 107 – 113 .
   Google Scholar
• Parmuchi , M.G. , Karszenbaum , H. and Kandus , P. 2002 . Mapping wetlands using multi-temporal RADARSAT-1 data and a decision-based classifier . Canadian Journal of Remote Sensing , 28 : 175 – 186 .
   Web of Science ®Google Scholar
• PCI Geomatica . 2009 . Geomatica OrthoEngine Course Guide , Richmond Hill , ON : PCI Geomatics Enterprises Inc .
   Google Scholar
• Penã-Barragán , M.J. , Ngugi , M.K. , Plant , R.E. and Six , J. 2011 . Object-based crop identification using multiple vegetation indices, textural features and crop phenology . Remote Sensing of Environment , 115 : 1301 – 1316 .
   Web of Science ®Google Scholar
• Pouncey , R. and Swanson , K. 1999 . ERDAS Manual , 5th , 540 Norcross , GA : ERDAS .
   Google Scholar
• Richter , R. 1990 . A fast atmospheric correction algorithm applied to LANDSAT TM images . International Journal of Remote Sensing , 11 : 159 – 166 .
   Web of Science ®Google Scholar
• Rydberg , A. and Borgefors , G. 2001 . Integrated method for boundary delineation of agricultural fields in multispectral satellite images . IEEE Transactions on Geoscience and Remote Sensing , 39 : 2514 – 2520 .
   Web of Science ®Google Scholar
• Schoenmakers , R.P.H.M. , Van Leeuwen , H.J.C. , Lemoine , G.G. and Nezry , E. Segmentation of combined high resolution optical and radar imagery for the determination of field inhomogenities . Proceedings of the IGARSS . August 8–12 1994 . pp. 2137 – 2139 . Pasadena , CA
   Google Scholar
• Serkan , M. , Musaoglu , N. , Kirkici , H. and Ormeci , C. 2008 . Edge and fine detail preservation in SAR images through speckle reduction with an adaptive mean filter . International Journal of Remote Sensing , 29 : 6727 – 6738 .
   Web of Science ®Google Scholar
• Skriver , H. , Mattia , F. , Satalino , G. , Balenzano , A. , Pauwels , V.R.N. , Verhoest , N.E.C. and Davidson , M. 2011 . Crop classification using short-revisit multitemporal SAR data . IEEE Journal of Selected Topics in Applied Earth Remote Sensing , 4 : 423 – 431 .
   Web of Science ®Google Scholar
• Smith , G.M. and Fuller , R.M. 2001 . An integrated approach to land cover classification: an example in the Island of Jersey . International Journal of Remote Sensing , 22 : 3123 – 3142 .
   Web of Science ®Google Scholar
• Stankiewicz , K.A. 2006 . The efficiency of crop recognition on ENVISAT ASAR images in two growing season . IEEE Transactions on Geoscience and Remote Sensing , 44 : 806 – 814 .
   Web of Science ®Google Scholar
• Turker , M. and Arikan , M. 2005 . Sequential masking classification of multi-temporal Landsat7 ETM+ images for field-based crop mapping in Karacabey, Turkey . International Journal of Remote Sensing , 26 : 3813 – 3830 .
   Web of Science ®Google Scholar
• Turker , M. and Ozdarici , A. 2011 . Field-based crop classification using SPOT4, SPOT5, IKONOS, and QuickBird imagery for agricultural areas: a comparison study . International Journal of Remote Sensing , 32 : 9735 – 9768 .
   Web of Science ®Google Scholar
• Wang , D. , Lin , H. , Chen , J. , Zhang , Y. and Zeng , Q. 2010a . Application of multi-temporal ENVISAT ASAR data to agricultural area mapping in the Pearl River Delta . International Journal of Remote Sensing , 31 : 1555 – 1572 .
   Web of Science ®Google Scholar
• Wang , L. , Gong , P. , Ying , Q. , Yang , Z. , Cheng , X. and Ran , Q. 2010b . Settlement extraction in the north china plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm . International Journal of Remote Sensing , 31 : 1411 – 1426 .
   Web of Science ®Google Scholar
• Weidner , U. Contribution to the assessment of segmentation quality for remote sensing applications . Proceedings of the 21st Congress for the International Society for Photogrammetry and Remote Sensing . July 3–11 . Beijing , , China
   Google Scholar
• Xiao , P. , Feng , X. , An , R. and Zhao , S. 2010 . Segmentation of multispectral high-resolution satellite imagery using log gabor filters . International Journal of Remote Sensing , 31 : 1427 – 1439 .
   Web of Science ®Google Scholar
• Yu , Q. , Gong , P. , Clinton , N. , Biging , G. and Schirokauer , D. 2006 . Object-based detailed vegetation mapping using high spatial resolution imagery . Photogrammetric Engineering and Remote Sensing , 72 : 799 – 811 .
   Web of Science ®Google Scholar
• Zhan , Q. , Molenaar , M. , Tempfli , K. and Shi , W. 2005 . Quality assessment for geo-spatial objects derived from remotely sensed data . International Journal of Remote Sensing , 26 : 2953 – 2974 .
   Web of Science ®Google Scholar
• Zhang , Y.J. 1996 . A survey on evaluation methods for image segmentation . Pattern Recognition , 29 : 1335 – 1346 .
   Web of Science ®Google Scholar
• Zhang , Y.J. A review of recent evaluation methods for image segmentation . International Symposium on Signal Processing and its Applications (ISSPA) . August 13–16 2001 . pp. 148 – 151 . Kuala Lumpur , , Malaysia
   Google Scholar