Automatic Interpretation of UltraCam Imagery by Combination of Support Vector Machine and Knowledge-based Systems

With the development of digital sensors, an increasing number of high-resolution images are available. Interpretation of these images is not possible manually, which necessitates seeking for practical, fast and automatic solutions to solve the environmental and location-based management problems. The land cover classification using high-resolution imagery is a difficult process because of the complexity of the landscapes, and the spatial and spectral resolution of images. The predecessor studies of land cover classification were done using statistical methods such as maximum likelihood classification. However, the newer studies apply the artificial intelligence techniques such as artificial neural networks and support vector machines as a substitute for classification applications. A major problem with using these models is that the user cannot easily understand the final rules. In this paper, a hybrid algorithm is proposed in order to obtain the needed data by the knowledge-based system from the input data set. The proposed algorithm is designed to get better training data and improvement of the learning system in semi-urban areas by classes covered by different material and colors.
Classification of the remote sensing images refers to separation of the similar spectral sets and division of the units with the same spectral behavior. In remote sensing, due to the large size of data, the processing procedure is costly. On the other hand, to achieve better results, it has been recommended to use various features in the training procedure, which will consequently incrementally increase the volume of processing. Accordingly, the use of object-based process can increase velocity and homogeneity of the final interpreted image by reducing the computational base units. In the field of feature generation, a hybrid feature including both region-based (by kernels) and object-based (by segments) strategy, has been employed in this study. In order to produce the training data, needless of determining that by the user, utilize the capacity of integration of the multi-source data by KBS based system. For this purpose, the ontology concept that applying by the knowledge-based rules was used. Then to improve the obtained training samples and compensate its defects in the expression of the target class properties, the correction step is done. In the other words, the automatic Knowledge-based method was performed to apply the ontological relationships in order to train and control the object-based support vector machine system.
In order to evaluate the proposed method, a set of test images from two different geographic regions were used for validation of the method. In each geographic region, it was attempted to select different test images (various scene features). On this basis, in the first group, three test images belong to a region in northern Iran and Bandar Anzali city, and the second group includes two images in Germany. The GSD (Ground Sampling Distance) of all the 5 test images is equal to 9 cm. Finally, the proposed method has achieved an average accuracy of 82/80% in all test images. The evaluation of the results showed that the proposed technique could be desirable as an automatic and semi-supervised method for interpreting high-resolution images of the semi-urban regions.