A novel approach for satellite image classification using local self-similarity

Extracting man-made objects in satellite images which are generated from the meter to sub-meter resolution plays an important role in remote satellite image analysis. However, spectral characteristics of urban land objects are so similar. So the classification accuracies are far from satisfactory by using only spectral information. As a result, researchers turn to incorporate geometrical information into satellite image classification. In this paper, we introduce a new local feature, namely local self-similarity(LSS) which captures internal geometric layouts of local self-similarities, into high spatial resolution images classification application. Our method captures self-similarity of color, edges, repetitive patterns and complex textures in a single unified way. With the help of Bag-of-Visual Words and SVMs, the proposed method per forms well. Experimental results on Quickbird-image data set show that the proposed local self-similarity representation yields better classification performance than the low-level features, such as the spectral and texture features.