پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,093,623 |
| تعداد دریافت فایل اصل مقاله | 97,198,230 |
Comparing pixel-based and object-based algorithms for classifying land use of arid basins (Case study: Mokhtaran Basin, Iran) | |||
| Desert | |||
| مقاله 12، دوره 24، شماره 1، شهریور 2019، صفحه 119-132 اصل مقاله (1.02 M) | |||
| نوع مقاله: Research Paper | |||
| شناسه دیجیتال (DOI): 10.22059/jdesert.2019.72446 | |||
| نویسندگان | |||
| Z. Rafieemajoomard1؛ M. Rahimi* 1؛ Sh. Nikoo1؛ H. Memarian2؛ S.H. Kaboli1 | |||
| 1Department of Combat Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran | |||
| 2Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran | |||
| چکیده | |||
| In this research, two techniques of pixel-based and object-based image analysis were investigated and compared for providing land use map in arid basin of Mokhtaran, Birjand. Using Landsat satellite imagery in 2015, the classification of land use was performed with three object-based algorithms of supervised fuzzy-maximum likelihood, maximum likelihood, and K-nearest neighbor. Nine combinations were examined in terms of scale level (SL10, SL30, and SL50) and the nearest neighborhood (NN3, NN5, and NN7) in an object-based classification. Ultimately, the validity was evaluated through the usage of two disagreement components including allocation disagreement and quantity disagreement. Results of maximum likelihood classification showed higher overall inaccuracycompared to images categorized based on fuzzy-maximum likelihood and object-based nearest neighbor algorithms. The SL30-NN3 object-based classifier decreased the quantity disagreement by 290% compared to the maximum likelihood and 265% compared to fuzzy-maximum likelihood classifiers. For allocation disagreement, these values were equal to 36% and 19%, respectively. Thus, object-based classification had a better performance in land-use classification of Mokhtaran basin. | |||
| کلیدواژهها | |||
| Maximum likelihood classifier؛ fuzzy-maximum likelihood classifier؛ K-nearest neighbor object-based classifier؛ Land use؛ Landsat imagery | |||
| مراجع | |||
|
Akbarpour, A. A., M. B. Sharifi, H. Memarian, 2006. The Comparison of Fuzzy and Maximum Likelihood Methods in Preparing Land Use Layer Using ETM+ Data (Case Study: Kameh Watershed). Range and Desert Research, 22; 27–38.
Blaschke, T., S. Lang, E. Lorup, J. Strobl, P. Zeil, 2000. Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications. Environmental Information for Planning, Politics and the Public 2; 555-570.
Blaschke, T., 2010. Object based Image analysis for Remote Sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65; 2-16.
http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004.
Bocco, M., G. Ovando, S. Sayago, E. Willington, 2007. Neural Network Model for Land Cover Classification from Satellite Images. Agricultura Técnica, 67; 414–421.
Baatz, M., A. Schape, 2000. Multiresolution Segmentation–an Optimization Approach for High Quality Multi-scale Image Segmentation. In: J. Strobl, T. Blaschke & G. Griesebner (eds.), Angewandte Geographische Informations verarbeitung XII, Wichmann Verlag, Heidelberg, pp. 12–23.
Brandt, T., P.M. Mather, 2016. 2nd ed., Classification Methods for Remotely Sensed Data. CRC press, Boca Raton, 376p.
Cohen, J., 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20; 37-46.
http://dx.doi.org/10.1177/001316446002000104.
Campbell, N. A., 1984. Some aspects of allocation and discrimination. In Multivariate Statistical Methods in Physical Anthropology. Springer, Dordrecht, p. 177–192. https://doi.org/10.1007/978-94-009-6357-3_12.
Chavez, P. S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multi-spectral data. Remote Sensing of Environment, 24; 459–479. http://dx.doi.org/_ 10.1016/0034-4257(88)90019-3.
Chavez, P., S. C. Sides, J. A. Anderson, 1991. Comparison of three different methods to merge multiresolution and multispectral data- landsat tm and spot panchromatic. Photogrammetric Engineering and Remote Sensing, 57; 295-303.
Casals-Carrasco, P., S. Kubo, B. B. Madhavan, 2000. Application of spectral mixture analysis for terrain evaluation studies. International Journal of Remote Sensing, 21; 3039-3055.
Costa, G. A. O. P., R. Q. Feitosa, T. B. Cazes, B. Feijó, 2008. Genetic Adaptation of Segmentation Parameters. Object-Based Image Analysis, Springer, Heidelberg, p. 679–695.
Chen, M., W. Su, L. Li, C. Zhang, A. Yue, H. Li, 2009. Comparison of pixel-based and object-oriented knowledge-based classification methods using SPOT5 imagery. WSEAS Transactions on Information Science and Applications, 6; 477-489.
De Kok, R., T. Schneider, U. Ammer, 1999. Object-Based Classification and Applications in the Alpine Forest Environment. International archives of photogrammetry and remote sensing, Valladolid, Spain. 32, part 7–4–3 Wg.
Drǎguţ, L., D. Tiede, S. R. Levick, 2010. ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24; 859-871. http://dx.doi.org/10.1080/13658810903174803.
Dean, A. M., G. M. Smith, 2003. An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities. International Journal of Remote Sensing, 24; 2905-2920.
Dingle Robertson, L., D. J. King, 2011. Comparison of pixel- and object-based classification in land cover change mapping. International Journal of Remote Sensing, 32; 1505-1529. http://dx.doi.org/ 10.1080/01431160903571791.
Dehvari, A., R. J. Heck, 2009. Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic layer. Geography and Regional Planning, 2; 86-96.
Duro, D. C., S. E. Franklin, M. G. Dubé, 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118; 259–272.
Exelis Visual Information Solutions Inc., 2015. Feature Extraction with Example-Based Classification Tutorial. In: Harris Geospatial Corporation. Eastman, J.R., 2016. IDRISI Terrset Manual. Clark Labs-Clark University: Worcester, USA, 391p.
Foody, G. M., N. A. Campbell, N. M. Trodd, d T. F. Wood, 1992. Derivation and applications of probabilistic measures of class membership from the maximum-likelihood. classification. Photogrammetric Engineering and Remote Sensing, 58; 1335-1341.
Flanders, D., M. Hall-Bayer, J. Pereverzoff, 2003. Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing, 29; 441–452. http://dx.doi.org/10.5589/m03-006.
Foody, M. G. 2004. Thematic Map Comparison. Photogrammetric Engineering & Remote Sensing, 70; 627-633.
Fathizad, H., D. Tazeh, S. Kalantari, 2016. Assessment of pixel-based classification (ARTMAP Fuzzy Neural Networks and Decision Tree) and object-oriented methods for land use mapping (Case Study: Meymeh, Ilam Province). Arid Biome, 5; 69-82.
Gao, Y., J. F. Mas, 2008. A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions. Earth Sciences, 2; 27-35.
Hay, G. J., G. Castilla, 2006. Object-Based Image Analysis: Strengths, Weaknesses, Opportunities and Threats (SWOT). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Salzburg, July 4, p. 4–5.
Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J.G., Gao, F., White, J.C., 2009. A new data fusion model for high spatial-and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113; 1613–1627.
https://doi.org/10.1016/j.rse.2009.03.007.
Khan, G. A., S. Khan, N. A. Zafar, S. Islam, F. Ahmad, M. ur Rehman, M. Ullah, 2012. A review of different approaches of land cover mapping, Life Science Journal, 9; 1023-1032.
Lillesand, M., W. R. Kiefer, 1994. Remote Sensing and Image Interpretation. 3th ed., John Wiley and Sons, New York, 750 p.
Lang, L., X. Wen, A. Gonzalez, D. Tan, J. Du, Y. Liang, D. Xiang, 2011. An object-oriented daytime land-fog-detection approach based on the mean-shift and full lambda-schedule algorithms using EOS/MODIS data. International Journal of Remote Sensing, 32; 4769-4785. http://dx.doi.org/10.1080/01431161.2010.489067.
Memarian, H., S. K. Balasundram, J. B. Talib, C. T. B. Sung, A. M. Sood, K. Abbaspour, 2012. Validation of ca-markov for simulation of land use and cover change in the langat basin, malaysia. Geographic Information System, 4; 542-554. http://dx.doi.org/_ 10.4236/jgis.2012.46059.
Memarian, H., S. K. Balasundram, R. Khosla, 2013. Comparison Between Pixel-and Object-Based Image Classification of a Tropical Landscape Using Système Pour l’Observation de la Terre-5 Imagery. Applied Remote Sensing, 7; 073512-073512. http://dx.doi.org/10.1117/1.JRS.7.073512.
Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, Q. Weng, 2011. Per-Pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115; 1145-1161.
Mathieu, R., J. Aryal, 2007. Object-based classification of ikonos imagery for mapping large-scale vegetation communities in urban areas. Sensors, 7; 2860–2880. http://dx.doi.org/10.3390/s7112860.
Platt, R. V., L. Rapoza, 2008. An evaluation of an object-oriented paradigm for land use/land cover classification. The Professional Geographer, 60; 87-100. http://dx.doi.org/10.1080/ 00330120701724152.
Pontius, R. G., 2000. Quantification error versus location error in comparison of categorical maps.
Photogrammetric engineering and remote sensing, 66; 1011–1016.
Pontius, R. G., W. Boersma, J. C. Castella, K. Clarke, T. de Nijs, C. Dietzel, P. H. Verburg, 2008. Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science, 42; 11–37.
Pontius, R. G., M. Millones, 2011. Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment. International Journal of Remote Sensing, 32; 4407–4429.
Rees, G., 1999. The Remote Sensing Data Book. Cambridge university press, Cambridge, 262 p.
Roy, D. P., M. A. Wulder, T. R. Loveland, C. E. Woodcock, R. G. Allen, M. C. Anderson, T. A. Scambos, 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, 145; 154-172. https://doi.org/10.1016/j.rse.2014.02.001.
Richards, J. A., X. Jia, 2006. Remote Sensing Digital Image Analysis. 4th ed., Springer, Berlin, 439 p. https://doi.org/10.1007/3-540-29711-1 .
Stephenson, D. B., 2000. Use of the “Odds Ratio” for diagnosing forecast skill. Weather and Forecasting, 15; 221-232.
https://doi.org/10.1175/1520-0434(2000) 015%3C0221:UOTORF%3E2.0.CO;2.
Singh, A., 1986. Change detection in the tropical forest environment of northeastern india using landsat. In: Eden MJ, Parry JT (eds), Remote Sensing and Tropical Land Management. Wiley, London, p. 237–254.
Wilks, D. S., 2011. Statistical Methods in the Atmospheric Sciences. Academic press, Amsterdam, 704 p.
Wang, Z., W. Wei, S. Zhao, X. Chen, 2004. Object-oriented classification and application in land use classification using SPOT-5 PAN imagery. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS ’04, Anchorage, Sept. 20–24, p. 3158–3160. https://doi.org/10.1109/IGARSS.2004.1370370.
Wang, F., 1990. Improving remote sensing image analysis through fuzzy information representation. Photogrammetric Engineering and Remote Sensing, 56; 1163–1169.
Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly, D. Schirokauer, 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72; 799-811. https://doi.org/10.14358/PERS.72.7.799.
Yan, G., J. F. Mas, B. H. P. Maathuis, Z. Xiangmin, P. M. Van Dijk. 2006. Comparison of pixel-based and object-oriented image classification approaches-a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27; 4039-4055. https://doi.org/10.1080/01431160600702632.
Zhang, J., G. M. Foody. 2001. Fully-fuzzy supervised classification of sub- urban land cover from remotely sensed imagery: statistical and artificial neural network approaches. International Journal of Remote Sensing, 22; 615–628.
https://doi.org/10.1080/01431160050505883.
Zanter, K., 2016, LANDSAT 8 (L8) Data Users Handbook (LSDS-1574 Version 2.0). United States Geological Survey, South Dakota, 98 p. | |||
|
آمار تعداد مشاهده مقاله: 597 تعداد دریافت فایل اصل مقاله: 493 |
|||