Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

Spectral properties of 1D extended Hubbard model from bosonization and time-dependent variational principle: applications to 1D cuprate

Recent ARPES experiments on doped 1D cuprates revealed the importance of effective near-neighbor (NN) attractions in explaining certain features in spectral functions. Here we investigate spectral properties of the extended Hubbard model with the on-site repulsion $U$ and NN interaction $V$, by employing bosonization analysis and the high-precision time-dependent variational principle (TDVP) calculations of the model on 1D chain with up to 300 sites. From state-of-the-art TDVP calculations, we find that the spectral weights of the holon-folding and $3k_F$ branches evolve oppositely as a function of $V$. This peculiar dichotomy may be explained in bosonization analysis from the opposite dependence of exponent that determines the spectral weights on Luttinger parameter $K_{\rho}$. Moreover, our TDVP calculations of models with fixed $U=8t$ and different $V$ show that $V\approx -1.7t$ may fit the experimental results best, indicating a moderate effective NN attraction in 1D cuprates that might provide some hints towards understanding superconductivity in 2D cuprates.Comment: 9 pages, 4 figure

Unextendible Maximally Entangled Bases in Cpd⊗Cqd\mathbb{C}^{pd}\otimes \mathbb{C}^{qd}

The construction of unextendible maximally entangled bases is tightly related to quantum information processing like local state discrimination. We put forward two constructions of UMEBs in $\mathbb {C}^{pd}\otimes \mathbb {C}^{qd}$($p\leq q$) based on the constructions of UMEBs in $\mathbb {C}^{d}\otimes \mathbb {C}^{d}$ and in $\mathbb {C}^{p}\otimes \mathbb {C}^{q}$, which generalizes the results in [Phys. Rev. A. 94, 052302 (2016)] by two approaches. Two different 48-member UMEBs in $\mathbb {C}^{6}\otimes \mathbb {C}^{9}$ have been constructed in detail