Combining Pixel- and Object-Based Approaches for Multispectral Image Classification Using Dempster-Shafer Theory

We propose an efficient framework for combining pixel and object-based approaches for Remote Sensing Image Classification using Support Vector Machines (SVMs) and Dempster-Shafer Theory of Evidence (DSTE). The pixel-based technique employs the multispectral information for assigning a pixel to a class according to the spectral similarities between the classes, and the object-based technique operates on objects consisting of many homogeneous pixels grouped together in a meaningful way through image segmentation. In order to manage the conflict that results from using both approaches, the final decision is performed using DSTE´s rule combination based on probabilistic output from both SVM classifiers. The evaluation test conducted on ETM+ image of Landsat-7 shows that the proposed technique achieved 95.24% classification accuracy. This performance is 5.78% higher than the better accuracy obtained by both SVMs. The proposed combination framework outperforms traditional methods by 2.14% accuracy´s margin.