Aerial image matching method based on HSI hash learning

Abstract

Specific to characteristics of aerial images like large size, high resolution and rich edges and texture, HSI hash learning matching algorithm for aerial image is proposed. Firstly, the original RGB component image is converted to HSI component image, then HSI-based local multi-feature descriptor is constructed. Secondly, in order to decrease the encoding mapping error in the traditional hash learning during model parameter optimization, objective function for each component image is constructed by the hash degree and the similarity information. And then, the projection matrix and bias threshold are determined. Finally, in the Hamming matching stage, the minimized loss distance function is defined and the spatial similarity between the Euclidean space and the Hamming space is preserved. Thus, the quantization loss in hash learning is reduced significantly. The proposed HSI hash learning algorithm evaluated on the standard dataset and actual aerial images demonstrates that it may significantly outperform the traditional algorithms in terms of efficiency and performance. As shown by experimental results, the matching accuracy of the proposed method is at least 20% higher than that of other binary descriptors, with desired computation time consumption.