Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Evaluating efficiency of object-based classification techniques used to extract land use from IKONOS satellite imageries

Document Type : Research Paper

Authors
Saeed salmani 1
Hamid Ebrahimy 1
Keyvan Mohammadzade 1
Khalil Valizadeh Kamran 2
1 Msc Student of Remote Sensing and Geographic Information System Faculty of Geography and Planning Department, University of Tabriz
2 Assosiate Professorof Remote Sensing and Geographic Information SystemFaculty of Geography and PlanningDepartment, University of Tabriz
10.22131/sepehr.2019.37520
Abstract
Extended Abstract
Introduction
With the advent of remote sensing technology, huge volume of remotely sensed data is now availablein different areas. As the fastest and the most cost-efficient method, satellite data is available for both researchers and responsible authorities seeking to produce land use (LU) maps. Compared to traditional methods, object based image analysis (OBIA) techniques use more comprehensive datasets,including geometric information (shape and placement of phenomena), digital elevation models, andvarious spectralindicesfor LU classification.Therefore, different OBIA methods have been widely used forclassification of satellite imageriesin different regions. Despite large amount of researches performed in this area, little attention has been paid to the systematic comparison ofdifferent object-based methods. Therefore, examining different techniques used for object-based processing of satellite imageries in diffrent situations can be considered as an appropriate research field for researchers.  The present studyexamines some powerful OBIA classification techniques such as threshold, nearest neighbor algorithm and fuzzy object based classification to determine the most suitable OBIA algorithm for classification of Ikonos satellite images.
 
Materials & methods
An Ikonos satellite imagery was used in this studywhich included red, green, blue and near-infrared bandswith spatial resolution of 4 m and a1 m resolutionpanchromatic band.Object based classification can be implemented in three general phases: segmentation, classification, and accuracy assessment.The present study has appliedmulti-resolution segmentation method in the segmentation phase. Three techniques ofthreshold, nearest neighbor algorithm and fuzzy based OBIA were also used for classification.
 
Results &discussion
The present study takes advantage of various features to extract land use classesfrom Ikonos satellite imageswith high level of accuracy.Textual information (Grey Level Co-occurrenceMatrix), mean of the imagery’s spectral bands, geometry (shape, density and asymmetry), and normalized difference vegetation index (NDVI)were among these features.Compared to threshold method,nearest neighbor algorithm withoverall accuracy of 92% and kappacoefficient of 0.9hada higher level of accuracy.Also, FOS algorithm was used to optimize the nearest neighbor technique. This algorithm optimizes intervals between the training samples using secondary information provided by the user.The eighteenth dimension, which contains the mean of spectral bands3 and 4, vegetation index, brightness, length to width ratio, indices of shape, compactness, asymmetry, texture information (homogeneityand contrast), were determined by FOS algorithmas the best dimension for extracting each LU classes. Finally,featuresproposed by FOS algorithm were used for image classification in nearest neighbor method.This optimizing process is considered to be one of the main reasons for superior performance ofnearest neighbor technique compared to threshold method.
 
Conclusion
In this research, three OBIA methods including threshold technique, nearest neighbor algorithm and fuzzy based OBIA algorithm were compared based on their capability in producing land use map from Ikonos satellite image. Identical ground control pointsof the study areawere used to classify and compare the results of these three OBIA classification methods.Finally, the best classification algorithmwas determinedbased on thevalues of accuracy assessment metrics including overall accuracy and kappa coefficient. Results indicate thatwith overall accuracy of 97%, and kappa coefficient of 0.95, fuzzy based OBIA classification algorithm has thehighest accuracy as compared to nearest neighbor algorithm and threshold method. Generally, the accuracy of fuzzy based OBIA classification method largely depends on the selection of appropriateclassification parameters and suitablealgorithm to obtain membership degrees.Investigating membership degree of effective parameters in the classification and using parameters with maximum degree of membership are considered to be two main reasons for achieving this high accuracy. Results of the present study indicate that fuzzy based OBIA techniqueis the best algorithm for classification ofIKONOS satellite images in the study area, andareas with similar conditions. This findingcanguide researchers and organizations producingLU map from IKONOS satellite imagery. Finally, investigating different techniques using satellite imageries (imageries with different spatial resolution, and received from areas with different land uses) is considered to be an appropriate area of study for OBIA researches.
Keywords
Segmentation
Threshold
Nearest Neighbor
Fuzzy membership
FOS algorithm
1. فیضی زاده, ب., بختیار, پیرنظر, زندکریمی و عابدی قشلاقی. (1394). ارزیابی استفاده از الگوریتم های فازی در افزایش دقت نقشه های کاربری اراضی استخراج شده با روش های پردازش شیءگرا. فصلنامه علمی-پژوهشی اطلاعات جغرافیایی «سپهر», 24(94), 107-117.‎
2. نیک‌فر، م.، ولدان زوج، م.، مختارزاده، م.، علیاری،م. 1394. طراحی یک پایگاه قوانین عارضه مبنا جهت کشف عارضه راه از تصاویر ماهواره‌ای با حد تفکیک مکانی بالا. مهندسی فناوری اطلاعات مکانی، (1)3: 77-95.
3. Baatz, M., Hoffmann, C. & Willhauck, G. (2008). Progressing from object-based to object-oriented image analysis. In Object-Based Image Analysis (pp. 29-42). Springer Berlin Heidelberg.
4. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I. & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of photogrammetry and remote sensing, 58(3), 239-258.
5. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), 2-16.
6. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E.  & Tiede, D. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191.
7. Chaudhuri, B. B. & Sarkar, N. (1995). Texture segmentation using fractal dimension. IEEE Transactions on pattern analysis and machine intelligence, 17(1), 72-77.
8. Chavez, P.S., Jr. 1998. An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data, Remote Sensing of Environment, Vol. 24, no. 3, pp. 459-479.
9. Chen, M., Su, W., Li, L., Zhang, C., Yue, A. & Li, H. (2009). Comparison of pixel-based and object-oriented knowledge-based classification methods using SPOT5 imagery. WSEAS Transactions on Information Science and Applications, 3(6), 477-489.
10. Chipman, J. W., Lillesand, T. M., Schmaltz, J. E., Leale, J. E. & Nordheim, M. J. (2004). Mapping lake water clarity with Landsat images in Wisconsin, USA. Canadian Journal of Remote Sensing, 30(1), 1-7.
11. Drăguţ, L. & Blaschke, T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81(3), 330-344.
12. Fassnacht, F. E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P. & Koch, B. (2014). Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154, 102-114.
13. Ghosh, A. & Joshi, P. K. (2014). A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high-resolution WorldView 2 imagery. International Journal of Applied Earth Observation and Geoinformation, 26, 298-311.
14. Li, M., Ma, L., Blaschke, T., Cheng, L. & Tiede, D. (2016). A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation, 49, 87-98.
15. Liu, Y., Li, M., Mao, L., Xu, F. & Huang, S. (2006). Review of remotely sensed imagery classification patterns based on object-oriented image analysis. Chinese Geographical Science, 16(3), 282-288.
16. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S. & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote sensing of environment, 115(5), 1145-1161.
17. Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm. CRC press.
18. Pal, M. & Mather, P. M. (2005). Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007-1011.
19. Strasser, T. & Lang, S. (2015). Object-based class modelling for multi-scale riparian forest habitat mapping. International Journal of Applied Earth Observation and Geoinformation, 37, 29-37.
20. Süzen, M. L. (2002). Data driven landslide hazard assessment using geographical information systems and remote sensing (Doctoral dissertation, Middle East Technical University).
21. Szuster, B. W., Chen, Q. & Borger, M. (2011). A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31(2), 525-532.
22. Tiede, D., Lang, S., Albrecht, F. & Hölbling, D. (2010). Object-based class modeling for cadastre-constrained delineation of geo-objects. Photogrammetric Engineering & Remote Sensing, 76(2), 193-202.
23. Wijaya, A., Budiharto, R. S., Tosiani, A., Murdiyarso, D. & Verchot, L. V. (2015). Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 557-573.
24. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M. & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), 799-811.
25. Yu, Q., Gong, P., Tian, Y. Q., Pu, R. & Yang, J. (2008). Factors affecting spatial variation of classification uncertainty in an image object-based vegetation mapping. Photogrammetric Engineering & Remote Sensing, 74(8), 1007-1018.
26. Zhang, Y. & Maxwell, T. (2006, May). A fuzzy logic approach to supervised segmentation for object-oriented classification. In ASPRS 2006, Annual Conference Reno, Nevada May (pp. 1-5).