Abstract—With the development of earth observation technology, a series of high spatial resolution remote sensing satellites has been successfully launched. The earth observation data, especially high spatial resolution remote sensing image, is becoming more accessible. Therefore, the ability and efficiency of high spatial resolution remote sensing image classification has become a prominent problem for its further applications. This paper presents an object-based image classification framework based on decision fusion for high spatial resolution remote sensing image. This framework mainly included three steps. Firstly, high spatial resolution remote sensing image was segmented by multi-resolution segmentation method. Secondly, the multi-source features of segmented regions were extracted and classified by Support Vector Machine classifier, respectively. Finally, the multi-source classification results were integrated by decision fusion and reclassification strategy. Quick-bird satellite data was performed to classify the land surface using the proposed classification framework; and the classification results using different feature spaces were compared. The results show that the classification method based on decision fusion takes fully advantage of multi-source region features and finally obtain higher classification precision.
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