A Comparative Review on Pixel-Based and Object-Based Approach for Land Cover (LC) Classification
Keywords:
Object-Pixel Based, SVM, DT, MLC, K-NN.Abstract
Image classification is one of the most basic techniques of digital image processing. This review focuses on the strengths and weaknesses of traditional pixel-based classification and object-based classification algorithms for the extraction of information from remotely sensed imageries. Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, and vegetation condition study and soil management. The LULC classification remains a difficult task and it is especially challenging in heterogeneous season landscapes where such maps are of great importance. Over the last decade, a number of classification algorithms have been developed for the analysis of remotely sensed data. The most algorithms are the pixel-based classification and object-oriented classification K-Nearest Neighbours (K-NN), Support Vector Machines (SVMs), the Decision Trees (DTs) and maximum likelihood classification (MLC) etc. Generally, classifiers information extraction can be divided into three categories: a] based on the type of learning (supervised and unsupervised), b] based on assumptions on data distribution (parametric and non-parametric) and, c] based on the number of outputs for each spatial unit (hard and soft). In this research, a comparative pixel-based and object-based land cover classification was developed in which advantages and disadvantages depending upon their area of application of both pixels and objects were different. This approach makes use of both pixel and object spectral features resulting from image segmentation through a comparative mechanism to resolve the problem of spectral confusion caused by reflectance similarity of some land cover types that traditional pixel-based classification cannot resolve.
References
- Alavipanah SK, Ghazanfari K, Khakbaz B,”Remote sensing and image understanding as reflected in poetical literature of Iran. In: Proceedings of remote sensing for science, education, and natural and cultural heritage”, 30th symposium of European association of remote sensing laboratories, 31st May–3rd June, UNESCO Headquarters, Paris, France (2010).
- Brian D. Wardlow, Stephen L. Egbert, ”Large area mapping using time-series MODIS 250 m NDVI data an assessment for the U.S. Central Great Plans” Elsevier 0034-4257s-@ (2007).
- Sunitha Abburu, PhD, Suresh Babu Golla, “Satellite Image Classification Methods and Techniques: A Review” International Journal of Computer Applications (0975 – 8887) Volume 119 – No.8, June (2015).
- Shridhar D. Jawak1, Prapti Devliyal2, Alvarinho J. Luis1, “A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications” Advances in Remote Sensing, 2015, 4, 177-195 (2015).
- M. Song, D. L. Civco & J. D. Hurd, “A competitive pixel-object approach for land cover classification”, International Journal of Remote Sensing, 26:22, 4981-4997 (2005).
- Volker Walter,” Object-based classification of remote sensing data for change detection” Elsevier ISPRS Journal of Photogrammetric & Remote Sensing 58 (2004).
- J. Senthilnath, Student Member,IEEE S.N.Omkar, V. Mani, tejovanthn, P.G. Diwakar, and Archana Shenoy B, Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images, journal of selected topics in applied earth observations and remote sensing, IEEE vol. 5, no. 3, June (2012).
- Xidong Jiang, Meizhen Lin, Junlei Zhao, Woodland cover change assessment using decision trees, support vector machines and artificial neural networks classification algorithms, Fourth International Conference on Intelligent Computation Technology and Automation IEEE (2011).
- Inmaculada Dopido, Alberto Villa, Member, IEEE, Antonio Plaza, Senior Member, IEEE, and Paolo Gamba, Senior Member, IEEE” A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification”, journal of selected topics in applied earth observations and remote sensing, vol. 5, no. 2, April (2012).
- Yuliya Tarabalka, Student Member, IEEE, Jon Atli Benediktsson, Fellow, IEEE, Jocelyn Chanson, Senior Member, IEEE, and James C. Tilton, Senior Member, IEEE Multiple Spectral–Spatial Classification Approach for Hyperspectral Data. IEEE On geosciences and remote sensing, IEEE vol. 48, no. 11, November (2010).
- Giorgos Mountrakis, Jungho Im, Caesar Ogole, “Support vector machines in remote sensing: A review” Elsevier ISPRS Journal of Photo grammetry and Remote Sensing 66 (2011).
- J R Otukei & T Blaschke, “Land Cover Change Assessment Using Decision Trees, support Vector machines and maximum likelihood classification algorithms, International Journal of Applied Earth Observation and Geo information 12S, pp.S27-S31, (2010).
- Bor-Chen Kuo, Member, IEEE, Hsin-Hua Ho, Cheng-Hsuan Li, Chih-Cheng Hung, Member, IEEE and Jin-Shiuh Taur, Senior Member IEEE, “A Kernel-Based Feature Selection Method For SVM with RBF Kernel for Hyperspectral Image Classification” IEEE journal of selected topics in applied earth observations and remote sensing, vol. 7, no. 1, January (2014).
- Dennis C. Duro, Steven E. Franklin Monique G. Dube 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, Elsevier (2011).
- Brian D. Wardlow, Stephen L. Egbert, ”Large area mapping using time-series MODIS 250 m NDVI data an assessment for the U.S. Central Great Plans” Elsevier 0034-4257 (2007).
- Isabel Luisa Castillejo-Gonzalez, Francisca Lopez-Granados, Alfonso Garcia-Ferrer, Jose Manuel Pena-Barraganb, Montserrat Jurado-Expositob, Manuel Sanchez de la Ordena, Maria Gonzalez-Audicanac, Object- and pixel-based analysis for mapping crops and their Agro environmental associated measures using Quick Bird imagery, Computers and Electronics In Agriculture Elsevier 68 207–215 (2009).
- George P. Petropoulos, Chariton Kalaitzidis, Krishna Prasad Vadrevu, support vector Machine and object-based classification for obtaining land-use/cover cartography from Hyperion Hyperspectral imagery, Elsevier (2015).
- Pooja A P, Jayanth J, Dr Shivaprakash Koliwad Professor and Head (R), Dept of ECE Malnad Collage of Engineering, Hassan, Karnataka, India Classification of RS data using Decision Tree Approach, International Journal of Computer Applications (0975 – 8887) Volume 23 – No.3, June (2011).
- Mahyat Shafapour Tehranya, Biswajeet Pradhan and Mustafa Neamah Jebuva, A comparative Assessment between object and pixel based classification approaches for land -use/land-Cover mapping using SPOT 5 imagery, Go-cart International, Vol. 29, No. 4, 351–369 (2014).
- Soe W. Myint, Patricia Gober, Anthony Brazel, Susanne Grossman-Clarke, Qihao Weng, Per-pixel vs. object-based classification of urban land cover extraction using high spatial Resolution imagery Remote Sensing of Environment, Elsevier 115 (2011).
- Xiaojiang Lia, Qingyan Menga, Xingfa Gua, Tamas Jancsoc, Tao Yua, Ke Wanga, and Sebastien Mavromatisd, “A hybrid method combining pixel-based and object-oriented methods And its application in Hungary using Chinese HJ-1 satellite images”, Vol. 34, No. 13,International Journal of Remote Sensing, Elsevier (2013).
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.