Abstract
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.
Highlights
With the development of digital sensors, an increasing number of high spatial resolution (HSR) remote-sensing images have become available [1]
In order to evaluate the efficiency of the proposed method in different feature spaces as an input of the classification method, new features were extracted from the images band and DSM data
The results demonstrated that the proposed technique is desirable as a semi-automatic method to interpret the high resolution of the semi-urban regions; still, this process can be completed in future studies
Summary
With the development of digital sensors, an increasing number of high spatial resolution (HSR) remote-sensing images have become available [1]. The availability and accessibility of vast amounts of high-resolution data have posed a challenge for remote-sensing image classification. Object-based image analysis (OBIA) techniques have emerged to address these issues [2]. These techniques have replaced the traditional pixel-based method as the new standard method [3] that will facilitate land-cover classification from HSR remote-sensing imagery. In addition to segmentation and sampling, all features, classification, and accuracy evaluation can bring uncertainty for OBIA In this regard, despite the fact that training samples or remote-sensing images may vary slightly, the accuracy assessment results of different commonly used classifiers are consistent with the expected conclusions from previous researches [6]. Compared to other factors, classifiers constitute a very important influential factor for supervised classification [2]
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