An improved Hybrid Spill-tree algorithm based on the signed method defined as Signed Hybrid Spill-tree (SHSPT) was proposed for target matching of remote sensing images. For establishing data and preprocessing, a data separate method based on a center point was proposed, the separated data were extracted by defining the center of dense data, and the edge data were abandoned. In the feature matching, binary array were used to express the data space and to mark the feature vector. Then, the bit operation was used to compute the distance between the feature vectors and to shorten the computing time. Finally, the feature matching algorithm was improved. The average value of the feature distance was used to replace the secondary characteristic distance from the Scale Invariant Feature Transform(SIFT)matching algorithm to obtain more matching points. The test results show that the computer memory by proposed algorithm is reduced 68% than that of traditional hybrid spill-tree method, and matching accuracy is closed to that of the traditional one. In addition, the method reduces 32.8% matching time. It solves the problems of remote sensing images in larger data amounts, higher dimensions, longer matching time and larger computer memory.