Tensor completion (TC), aiming to recover original high-order data from its degraded observations, has recently drawn much attention in hyperspectral images (HSIs) domain. Generally, the widely used TC methods formulate the rank minimization problem with a convex trace norm penalty, which shrinks all singular values equally, and may generate a much biased solution. Besides, these TC methods assume the whole high-order data is of low-rank, which may fail to recover the detail information in high-order data with diverse and complex structures. In this paper, a novel nonlocal low-rank regularization-based TC (NLRR-TC) method is proposed for HSIs, which includes two main steps. In the first step, an initial completion result is generated by the proposed low-rank regularization-based TC (LRR-TC) model, which combines the logarithm of the determinant with the tensor trace norm. This model can more effectively approximate the tensor rank, since the logarithm function values can be adaptively tuned for each input. In the second step, the nonlocal spatial-spectral similarity is integrated into the LRR-TC model, to obtain the final completion result. Specifically, the initial completion result is first divided into groups of nonlocal similar cubes (each group forms a 3-D tensor), and then the LRR-TC is applied to each group. Since similar cubes within each group contain similar structures, each 3-D tensor should have low-rank property, and thus further improves the completion result. Experimental results demonstrate that the proposed NLRR-TC method outperforms state-of-the-art HSIs completion techniques.
On 22 December 2020, HISEA-1, the first C-band SAR small satellite for ocean remote sensing, was launched from the coastal Wenchang launch site. Though small in weight, the images it produced have a high spatial resolution of 1 m and a large observation width of 100 km. The first batch of images obtained within the first week after the launch confirmed the rich information in the data, including sea ice, wind, wave, rip currents, vortexes, ships, and oil film on the sea, as well as landmark buildings. Furthermore, geometric characteristics of sea ice, wind vector, ocean wave parameter, 3D features of buildings, and some air-sea interface phenomena in dark spots could also be detected after relevant processing. All these indicate that HISEA-1 could be a reliable, remarkable, and powerful instrument for observing oceans and lands.
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