An optimal-truncation-based tucker decomposition method for hyperspectral image compression

Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, a novel compression method based on optimal-truncation tucker decomposition for HSI is proposed. HSI tensor is firstly decomposed into complete core tensor. And then core tensor and factor matrices are truncated according to the optimal number of components of core tensor along each mode (NCCTEM), which is determined by the proposed criterion for the optimal NCCTEM and searching strategy. Experimental results show that the proposed method has the excellent reconstruction comparable to the traditional compression methods. Furthermore, it significantly reduces the compression and decompression time.