Abstract
The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process. In this study, an inclusive semi-automatic method for image classification is offered, which presents the configuration of the related fuzzy functions as well as fuzzy rules. The produced results are compared to the results of a normal classification using the same parameters, but with crisp rules. The overall accuracies and kappa coefficients of the presented method stand higher than the check projects.
Highlights
With the development of satellite images to provide finer spatial resolutions, they can provide finer more details in urban mapping [1]
Considering the existence of extreme level of detail in very high resolution urban satellite images (VHRSI), object-based methods are being increasingly employed for image classification since they have a higher resemblance to human interpretation skills and in object-based image analysis, object characteristics such as shape, texture, topological information, and spectral response can be used [3,4]
In object-based image classifications, an image is divided into non-overlapping segments which are assigned to different classes using specific methods; for example, [5] presented a method for object-based image classification using a neural network
Summary
With the development of satellite images to provide finer spatial resolutions, they can provide finer more details in urban mapping [1]. Considering the existence of extreme level of detail in very high resolution urban satellite images (VHRSI), object-based methods are being increasingly employed for image classification since they have a higher resemblance to human interpretation skills and in object-based image analysis, object characteristics such as shape, texture, topological information, and spectral response can be used [3,4]. In object-based image classifications, an image is divided into non-overlapping segments which are assigned to different classes using specific methods; for example, [5] presented a method for object-based image classification using a neural network. They used a kernel called ―cloud base‖. Object-based image analysis is not mature enough to be used in automatic image analysis, it is still very promising [5]
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