Satellite image-based ancient dwelling fingerprint detection algorithm

Abstract

The paper proposes an elastic grid technique to separate satellite target image into some intersections of characteristic rows and columns, which can extract more local texture features. Then, the relevant statistics of gray level co-occurrence matrix (GLCM) reflected the regional global texture features are used to generate some feature cells, which can be merged into a fingerprint array. The fingerprint can preserve the global and local features of the target satellite image. Because these cells have different ranges and variances, each cell can be projected into a Gauss kernel space by using its relevant Gauss function. The similarity of fingerprint cell components can be calculated by the product of fingerprint cells by using Gauss measure. The similarity of fingerprint array can be calculated as the sum of similarities of fingerprint cells. The area algorithm and shape algorithm can be used to preliminary separate the dwelling targets from village satellite images, and the preliminary detection accurate rate is higher. When the dwelling fingerprint algorithm based on elastic grid and GLCM features is used, the accurate chronicle classification rate can reach more than 85.2%.