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 inaccuracy compared 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.
خلاصه ماشینی:
ir Desert 24-1 (2019) 119-132 Comparing pixel-based and object-based algorithms for classifying land use of arid basins (Case study: Mokhtaran Basin, Iran) Z.
H. Kabolia a Department of Combat Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran b Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran Received: 16 September 2018; Received in revised form: 30 October 2018; Accepted: 20 November 2018 Abstract 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.
Results of maximum likelihood classification showed higher overall inaccuracy compared to images categorized based on fuzzy-maximum likelihood and object-based nearest neighbor algorithms.
Land use and land cover classification with satellite image application can be classified into two universal techniques, such as pixel-based and object-based classifications.
Robertson and King (2011) used the Landsat 5 imageries through pixel-based and object-based classification algorithms to classify different types of agriculture land covers during two time periods.
Results confirmed a higher capability of the object-based method, in comparison to the pixel-based algorithms, for land use classification.
Such extra information gives the object-based image analysis a potential to make land cover thematic maps with greater accuracies, compared to those created by conventional pixel-based technique (Gao and Mas, 2008).
Comparison of pixel- and object-based classification in land cover change mapping.