Multiagent Object-Based Classifier for High Spatial Resolution Imagery

Abstract

Object-based classification, including object-based segmentation and classification, has been applied for the classification of high spatial resolution imagery due to the increase in the spatial resolution and the limited spectral resolution. Because of the independent design of the object-based segmentation and classification in many of the traditional object-based classification methods, additional work is required to select the appropriate segmentation algorithms to match the classification algorithms. The object-based segmentation algorithms, e.g., the fractal net evolution approach (FNEA), have been successfully utilized to provide the homogeneous regions, and are the basis of object-based classification. However, the traditional FNEA algorithm is greatly influenced by the global control strategy of the region-growing procedure. In addition, the existing object classification methods take little account of the object context information, which is important for high spatial-resolution image interpretation. To improve the accuracy of the object-based classification, in this paper, a multiagent object-based classification framework (MAOCF) for high-resolution remote sensing imagery is proposed. The proposed approach avoids the issue of segmentation algorithm selection by unifying the processing of object-based segmentation and classification through the use of a 4-tuple agent model. In the uniform framework, a multiagent object-based segmentation (MAOS) algorithm is proposed to optimally control the procedure of object merging. In addition, a MAOC is proposed to utilize the contextual information from the surrounding objects by taking advantage of the benefits of a multiagent system, e.g., strong interaction, high flexibility, and parallel global control capability. Due to the characteristics of a multiagent system, MAOCF has the potential for a parallel computing ability. Three experiments with different types of images were performed to evaluate the performance of MAOS and MAOC in comparison to other segmentation and classification algorithms: 1) mean-shift segmentation; 2) FNEA; 3) recursive hierarchical segmentation; and 4) the majority voting object-based classification method. The experimental results demonstrate that MAOS and MAOC give a stable performance with high spatial resolution remote-sensing imagery, and are competitive with the other methods.