A comparative evaluation of Pixel-Based and Object-Oriented processing techniques, used for the classification of Aster Satellite imageries and extracting agricultural and orchard maps in the Eastern Margin of Urmia Lake

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Faizizadeh Bakhtiar

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Journal: GEOGRAPHICAL DATA Year:2019 | Volume:28 | Issue:109 Page(s): 167-183

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Title

A comparative evaluation of Pixel-Based and Object-Oriented processing techniques, used for the classification of Aster Satellite imageries and extracting agricultural and orchard maps in the Eastern Margin of Urmia Lake

Author(s)

Faizizadeh Bakhtiar | Issue Writer Certificate

Keywords

Agricultural land use subQ1

classQ1

PixelQ1

Based and ObjectQ1

Oriented classification methodsQ1

Aster imagesQ1

Abstract

Introduction Nowadays, detailed land cover and land use information is considered to be an important research topic in geosciences, environmental changes and natural resources. In this regard, monitoring agricultural land-use provides essential information for land use planners and decision makers. Multiple methods are used for monitoring agricultural land use from remotely sensed images. Object-oriented techniques used for processing satellite images makes high accuracy recognition of various land use patterns possible. Compared to traditional Pixel-based approaches, these techniques reach a higher accuracy in extracting land-use information from satellite imageries using geometric information and features of different phenomena. In fact, object oriented methods relay on processing unit or image objects made by the integration of homogenized Pixels in the segmentation process. Once segments are formed, various indices and spatial information like texture, pattern, form, content, and etc. are applied on processing units. In this way, identifying appropriate land use classes based on geometric features becomes possible. As compared to traditional Pixel-based methods, these methods are more flexible and thus can apply a combination of spectral and spatial information. The main goal of the present article is to develop land-use maps and to evaluate agricultural activities in eastern basin of Urmia Lake using object-oriented processing techniques. Study area and martial The study area was chosen based on a mixture of agricultural land use, human settlements, and salt marshes in Urmia lake eastern margins. The main goal of the present study was to produce accurate maps of agricultural systems in Urmia lake eastern margins. Thus, various agricultural land uses were extracted from satellite imageries with an emphasis on orchard land use classes. Training data were collected through field operation using GPS. Moreover, 1: 25000 scale topographic maps were used for geometric correction and rectifying of the satellite images. Methods and techniques Various agricultural and orchard land use were extracted from Aster satellite imageries received in 2016. In pre-processing stage, geometric correction including geo-referencing, orthorectification and atmospheric corrections were performed on imageries. In processing stage, detection functions were applied and images were then classified according to the research goals based on Pixel-Based and Object-oriented algorithms. In this regard, maximum likelihood, parallelepiped, and minimum distance algorithms were used to classify images. Segmentation process was performed based on homogeneity, shape and compactness parameters. Accordingly, the geometric and spectral algorithms were used for modeling each class in object-oriented environment and object-oriented classification was applied based on nearest neighbor algorithm. Results Using object oriented and Pixel based processing techniques, four land use maps were extracted. In order to evaluate and compare final results, overall accuracy and Kappa coefficients were extracted for each algorithm. Results indicate that among Pixel-based classification algorithms, maximum likelihood algorithm with overall accuracy of 87. 67 percent and kappa coefficient of 0. 86 is more accurate than other methods. However, with a Kappa coefficient of about 0. 93 and overall accuracy of 94. 20 percent, this algorithm has a lower accuracy level as compared to object-oriented methods. Discussion and conclusion Results indicate that compared to Pixel based techniques, object oriented processing techniques possesses a higher potentiality for extracting agricultural land use. The main advantage of object oriented methods is that they employ a combination of spatial information, spectral information and integrate them with GIS and remote sensing datasets. Moreover, using texture and shape algorithms in object based classification leads to improved accuracy of land use maps. Besides, it is possible to improve the accuracy of results using effective techniques in object oriented classification. According to research findings, object oriented techniques provide an effective method for classification of satellite imageries and extraction of land use maps. It is possible to use these techniques in landscape planning, natural resources, regional land-use and land-cover changes, sustainability of land cover, and etc.

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APA: Copy

Faizizadeh, Bakhtiar. (2019). A comparative evaluation of Pixel-Based and Object-Oriented processing techniques, used for the classification of Aster Satellite imageries and extracting agricultural and orchard maps in the Eastern Margin of Urmia Lake. GEOGRAPHICAL DATA, 28(109 ), 167-183. SID. https://sid.ir/paper/253296/en

Vancouver: Copy

Faizizadeh Bakhtiar. A comparative evaluation of Pixel-Based and Object-Oriented processing techniques, used for the classification of Aster Satellite imageries and extracting agricultural and orchard maps in the Eastern Margin of Urmia Lake. GEOGRAPHICAL DATA[Internet]. 2019;28(109 ):167-183. Available from: https://sid.ir/paper/253296/en

IEEE: Copy

Bakhtiar Faizizadeh, “A comparative evaluation of Pixel-Based and Object-Oriented processing techniques, used for the classification of Aster Satellite imageries and extracting agricultural and orchard maps in the Eastern Margin of Urmia Lake,” GEOGRAPHICAL DATA, vol. 28, no. 109 , pp. 167–183, 2019, [Online]. Available: https://sid.ir/paper/253296/en

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