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Open AccessArticle

Development of an Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner

1
Geomatics Engineering Faculty, K.N. Toosi University of Technology, Tehran 1996715433, Iran
2
Department of Geomatics Engineering, University of Tabriz, Tabriz 5166616471, Iran
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 398; https://doi.org/10.3390/ijgi8090398
Received: 29 July 2019 / Revised: 16 August 2019 / Accepted: 2 September 2019 / Published: 5 September 2019
For an accurate interpretation of high-resolution images, correct training samples are required, whose automatic production is an important step. However, the proper way to use them and the reduction of their defects should also be taken into consideration. To this end, in this study, the application of different combinations of training data in a layered structure provided different scores for each observation. For each observation (segment) in a layer, the scores corresponded to the obtained misclassification cost for all classes. Next, these scores were properly weighted by considering the stability of different layers, the adjacency analysis of each segment in a multi-scale manner and the main properties of the basic classes. Afterwards, by integrating the scores of all classes weighted in all layers, the final scores were produced. Finally, the labels were achieved in the form of collective wisdom, obtained from the weighted scores of all segments. In the present study, the aim was to develop a hybrid intelligent system that can exploit both expert knowledge and machine learning algorithms to improve the accuracy and efficiency of the object-based classification. To evaluate the efficiency of the proposed method, the results of this research were assessed and compared with those of other methods in the semi-urban domain. The experimental results indicated the reliability and efficiency of the proposed method. View Full-Text
Keywords: object-based analysis; remote-sensing classification; fusion decision; support vector machines (SVMs); high spatial-resolution images object-based analysis; remote-sensing classification; fusion decision; support vector machines (SVMs); high spatial-resolution images
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Kiani, A.; Ebadi, H.; Farnood Ahmadi, F. Development of an Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner. ISPRS Int. J. Geo-Inf. 2019, 8, 398.

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