Subpixel mapping (SPM) aims to reconstruct a subpixel-level class distribution map from the pixel-level abundance maps, which is an under-determined problem that has nonunique solutions. To address this, the spatiotemporal SPM uses the abundance, spatial, and temporal constraints to reduce the uncertainty of the mapping solutions, so the spatiotemporal SPM is essentially a constrained optimization problem. However, it is hard to find the optimal weighting parameters to combine the three joint constraints. In addition, the existing spatiotemporal SPM methods mainly use the temporal information either for the unchanged subpixels detection or for the subpixel classification, which is insufficient in the utilization of the temporal information. In this article, a novel spatiotemporal SPM algorithm based on multiobjective optimization (STSPM_MO) is proposed. STSPM_MO is composed of an unchanged subpixels detection stage and a multiobjective spatiotemporal mapping stage. In the former stage, the historical thematic map is used for identifying the unchanged subpixels. In the latter stage, the historical thematic map is further used for providing the temporal dependence, so that the temporal information can be more fully utilized. Moreover, to solve the constrained optimization problem of the spatiotemporal SPM, the abundance, spatial, and temporal constraints are modeled as three objectives and are dynamically fused through the subfitness-based multiobjective evolution, to generate the optimal subpixel classification map. Both synthetic and real-data experiments have been conducted, and the results show the proposed method, and its two variants are superior, stable, and effective.

Two-factor authentication provides a strong defense against account compromise. However, traditional two-factor authentication schemes cannot provide users with much flexibility and fine-grained authorization. In this work, we present an efficient design ofAttribute-BasedPasswordAuthenticatedKeyExchange (AB-PAKE) protocol, ensuring that only two legitimate users with desired attributes and correct passwords can establish a shared session key. We, for the first time, tackle the problem of “how to enhance a peer-to-peer PAKE scheme by using a storage device (e.g., a smart-phone, a USB token, or a personal computer that the user logs in), such that even if ephemeral secret keys of two participants have been leaked, it still provides user privacy protection and truly two-factor security”. AB-PAKE works well in peer-to-peer (i.e., end-to-end) scenarios where the participants expect to hide their real identity information and the peer is enforced to satisfy the defined conditions (aka authentication policy). It achieves flexibility, privacy preservation, and dynamic access control lacking in prior authentication proposals. In addition, our work mitigates a practical threat in authenticated key exchange schemes, namely, the ephemeral secret leakage attack. We aim to increase the attack difficulty and limit password leakage even if the user’s long-term key or ephemeral key is leaked. The proposed protocol is also round-optimal, i.e., it is a single-round protocol consisting of only two message flows among the parties. Our new construction of AB-PAKE protocol reduces the number of pairing operations to be constant and supports richer policies. Provable security and practicality are demonstrated by comprehensive analysis.

Subpixel mapping (SPM) technology is an effective way to account for the distribution of the component objects within the mixed pixels and can alleviate the mixed pixel problem to some degree. However, the traditional SPM methods rely on only a single coarse-resolution image, and the limited information source can lead to great uncertainty. The rapid development of Earth observation systems has resulted in many fine spatial resolution remote sensing images now being accessible. Spatiotemporal SPM approaches utilize the finer spatial distribution of a historical thematic map to provide temporal prior information for the mapping process. However, spatiotemporal SPM is essentially a constrained optimization problem that aims to predict the optimal class distribution map subject to the abundance, spatial, and temporal constraints. It is a challenging task to properly model and optimize the multiple constraints. In this article, a multiobjective memetic spatiotemporal SPM (MOMSPM) framework is proposed. This model transforms the data fidelity term, spatial prior term, and temporal prior term into a multiobjective optimization problem (MOP) to discard the sensitive regularization parameters. The multiobjective model realizes the fusion of abundance, spatial, and temporal information. To optimize the three objective functions simultaneously, MOMSPM provides a multiobjective memetic algorithm framework in which the global multiobjective search method [multiobjective evolutionary algorithm based on decomposition (MOEA/D)] combines two commonly employed single-objective local search operators (geospatial distribution preference (GSDP) local search and maximum a posteriori (MAP)-based local search). The GSDP and MAP operators are employed to refine the solution and achieve an improved outcome. The hybridized method provides powerful search ability and achieves a good balance between the three objective functions. Experiments on synthetic and real datasets prove that the proposed method is superior to the state-of-the-art spatiotemporal SPM methods.

Conventional subpixel mapping (SPM) is performed based on the abundance maps obtained by spectral unmixing (SU), to interpret the mixed pixels and improve the mapping resolution for hyperspectral remote-sensing imagery. However, the SU and SPM tasks are separately conducted, so that the unmixing error is propagated to SPM, and the mapping result is strongly reliant on the quality of the abundance maps. In this article, a novel joint SPM and SU framework (MO_SUSM) based on multiobjective optimization is proposed to simultaneously perform unmixing and mapping. Specifically, the multiobjective joint optimization model with a data fidelity term and a Laplacian prior term is constructed for SU and SPM. For the data fidelity term, since the unmixing result can be recovered by downsampling the mapping result, the unmixing model is joined with the mapping model by the downsampling matrix, so that the reconstruction errors of the unmixing and mapping results can be minimized together. Meanwhile, the Laplacian prior term is used to maximize the spatial dependence of the mapping result and provide the spatial constraint for SU. In addition, the multiobjective optimization algorithm with local search is designed to search for the optimal unmixing and mapping results that can balance the objective terms. Since the two objective terms are dynamically integrated during optimization, there is no need to set sensitive weights for the objectives combination. Four experiments were conducted on hyperspectral images of various data sources, including ground, airborne, and satellite images. The unmixing results show that MO_SUSM can reduce the unmixing error and can improve the quality of the abundance maps. The mapping results show that MO_SUSM can alleviate the dependence of SPM on the abundance maps and can improve the mapping accuracy.

Subpixel mapping (SPM) is a useful technique that can interpret the spatial distribution inside mixed pixels and produce a finer-resolution classification map for hyperspectral remote-sensing imagery. However, SPM is essentially an ill-posed problem that requires additional information to produce the unique solution. The limited information of a single image is insufficient to make the mapping problem well posed, whereas the complementary spatial information of multiple shifted images is able to reduce the uncertainty and generate an accurate map. The maximum a posteriori model is a feasible way to incorporate auxiliary information for SPM with multiple shifted images, but it introduces a sensitive regularization parameter, which is difficult to preset. Furthermore, the fixed parameter in the iterations influences the incorporation of the multiple images and the spatial prior. In this article, to address these issues, a multiobjective SPM framework for use with multiple shifted hyperspectral images (MOMSM) is proposed. In the proposed algorithm, a multiobjective model consisting of two objective functions, i.e., data fidelity and spatial prior terms, is constructed to transform the SPM into a multiobjective optimization problem, to get rid of the sensitive regularization parameter. To simultaneously optimize the two objective functions, a multiobjective memetic algorithm with a local search operator and an adaptive global replacement strategy is proposed. The multiple images and spatial information can be dynamically fused and the optimal mapping solution with a good balance between the two objectives can be finally obtained. Experiments conducted on both synthetic and real data sets confirm that the proposed method outperforms the other tested SPM algorithms.

Sub-pixel mapping (SPM) can interpret the sub-pixel spatial distribution of land-cover classes in hyperspectral image, which is an ill-posed problem due to the inadequate information of a single image. Auxiliary information provided by multiple shifted (MS) images can make SPM problem well-posed and improve mapping accuracy. The maximum a posteriori (MAP) technique can incorporate the auxiliary information of MS images, but it introduces a fixed weight parameter to fuse the auxiliary information and spatial prior information, heavily influencing the mapping result. This paper proposed a novel SPM method to model the auxiliary information and spatial prior information into two objective functions, which can be simultaneously optimized by the devised multiobjective evolutionary algorithm. Therefore, there is no need of weight parameter, and the two objective functions can be intelligently integrated during the evolution. Experimental results and parameter analysis have indicated the superiority of the proposed method.

Subpixel mapping (SPM) of remote sensing imagery is aimed at generating a classification map with a finer spatial resolution based on the abundance maps. The sparse subpixel mapping (SSM) method reformulates the SPM problem into a spatial pattern linear regression problem based on the preconstructed subpixel patch dictionary. However, in the SSM model, the optimization of the L0-norm is a nonconvex NP-hard problem, so the L1-norm is used to replace the L0-norm to obtain an approximate solution, and the selection of the optimal weight parameter between multiple terms is difficult. Thus, in this paper, a novel multiobjective SSM (MOSSM) framework for remote sensing imagery is proposed, which transforms the SSM problem into a multiobjective optimization problem. In MOSSM, first, the sparsity term is accurately modeled using the L0-norm instead of the L1-norm to avoid the potential errors caused by the L1-norm, and an evolutionary algorithm is used to directly optimize the L0-norm. Second, a subfitness-based multiobjective evolutionary algorithm is employed to simultaneously optimize the fidelity term, the sparsity term, and the spatial prior term, and to generate a set of optimal sparse coefficients to balance these three terms. Thus, there is no need to determine sensitive weight parameters. Finally, two spatial prior terms, which can be applied to the overcomplete dictionary, are presented in the proposed MOSSM-TV and MOSSM-L algorithms to incorporate the spatial correlation of subpixels. Experiments were conducted with two synthetic images and two real data sets, and the results were compared with those of ten other SPM algorithms to demonstrate the effectiveness of the proposed method.