关键词: 矩形算法, 灰度共生矩阵（GLCM）, 弹性网格, 指纹, 民居分类

Abstract: House goals are separated from satellite image by using rectangle algorithm based on area feature and shape feature, and the mistakenly identified objects need to be eliminated from all separated objects based on their different texture features. Rotating, enlarging and cutting operations need to be designed to make those separated objects standardize with same size and direction. Elastic grid technique can choose several characteristic lines and columns of the image, and the intersections of them can partition an image into several characteristic grids. The classic relevant statistics of gray level co-occurrence matrix (GLCM) of each grid are computed to generate a characteristic fingerprint array, which comprehensively reflect the local texture features of the grid. The combination of fingerprint arrays of all grids can generate the fingerprint vector of an image, which can reflect global features of the image. The feature vector generated from improved elastic grid partition and GLCM statistic can simultaneously characterize the local texture features and global statistic features of an image. Ancient houses are accurately identified and classified by the similarity comparison to house samples of different periods based on their characteristic fingerprint vectors. Experiments show that the correct rate of separated house targets is about 86.9% by using rectangle algorithm, and using the house fingerprint algorithm based on elastic grid partition and GLCM statistics, the correct preliminary classification rate of ancient houses is more than 97.4%.

Key words: rectangle algorithm, gray level co-occurrence matrix (GLCM), elastic grid, fingerprint, house classification