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Segmentation and classification are important tasks in remote sensing image analysis. Recent research shows that images can be described in hierarchical structure or regions. Such hierarchies can produce the state-of-the-art segmentations and can be used in the classification. However, they often contain more levels and regions than required for an efficient image description, which may cause increased computational complexity. In this letter, we propose a new hierarchical segmentation method that applies graph Laplacian energy as a generic measure for segmentation. It reduces the redundancy in the hierarchy by an order of magnitude with little or no loss of performance. In the classification stage, we apply local self-similarity feature to capture the internal geometric layouts of regions in an image. By incorporating advantages from both semantic hierarchical segmentation and local geometric region description, we have achieved better performance than those from the methods being compared. In the experimental section, we validate the effectiveness of our method by showing results on QuickBird and GeoEye-1 image data sets.