High-resolution satellite image classification and segmentation using Laplacian graph energy

Many segmentation algorithms describe images in terms of a hierarchy of regions. Although such hierarchies can produce state of the art segmentations and can be used in the classification, they often contain more data than is required for an efficient description which cause increased complexity and time cost. In this paper, we proposed a new hierarchical segmentation method which apply Laplacian graph energy as a generic measure to reduce the number of levels and regions in the hierarchy by an order of magnitude with little or no loss in performance. We apply our method in remote sensing image analysis.