Publication Date:
2017-12-30
Description:
In our previous work, a tensor decomposition-based anomaly detection algorithm has been proposed. However, determining $K_{1}$ , $K_{2}$ , and $K_{3}$ (i.e., the major principal component numbers along the three modes of hyperspectral data) has not been settled satisfactorily. In this letter, a fast and adaptive method for determining $K_{1}$ , $K_{2}$ , and $K_{3}$ is proposed. In the proposed method, the determination problem is converted into an optimization problem by constructing the energy function by maximizing the anomalous degree of the reconstructed anomaly data in both spectral and spatial domains. In order to reduce the computational complexity, a fast initialization strategy is introduced to initialize those parameters in the feature space directly. In addition, to avoid the problem of parameter selection, an adaptive strategy is utilized. Furthermore, $K_{1}$ and $K_{2}$ are considered to be independent, making the degree of freedom of the three parameters conform with the actual. Experiments with three hyperspectral data sets reveal that the proposed method works effectively.
Print ISSN:
1545-598X
Electronic ISSN:
1558-0571
Topics:
Architecture, Civil Engineering, Surveying
,
Geography
,
Geosciences
Permalink
