With the development of Earth observation technology, more multisource remote sensing (RS) images are obtained from various satellite sensors and significantly enrich the data source of change detection (CD). However, the utilization of multisource bitemporal images frequently introduces challenges during featuring or representing the various physical mechanisms of the observed landscapes and makes it more difficult to develop a general model for homogeneous and heterogeneous CD adaptively. In this article, we propose an adaptive spatial-spectral transformer change detection network based on spectral token guidance, named STCD-Former. Specifically, a spectral transformer with dual-branch first encodes the diverse spectral sequence in spectral-wise to generate a corresponding spectral token. And then, the spectral token is used as guidance to interact with the patch token to learn the change rules. More significantly, to optimize the learning of difference information, we design a difference amplification module to highlight discriminative features by adaptively integrating the difference information into the feature embedding. Finally, the binary CD result is obtained by multilayer perceptron (MLP). The experimental results on three homogeneous datasets and one heterogeneous dataset have demonstrated that the proposed STCD-Former outperforms the other state-of-the-art methods qualitatively and visually.
With the development of display technology, more displays can cover the wider gamut, but most of the content they show is based on a small gamut. It is significant to employ gamut expansion (GE) to expand the small gamut images to a wider target gamut. Most of the existing GE methods only use global or local operations to realize the mapping from small gamut to wide gamut. However, the utilization of both global information and local feature is important for GE. In this article, the authors propose a combined global‐local gamut expansion network (G‐LGENet) for mapping the input standard RGB (sRGB) images to wider ProPhoto RGB space. In G‐LGENet, the global colour mapping module first extracts and fuses the global colour priors and learns the mapping of colour information for the corresponding pixels. And then, the local enhancement (LE) is designed to extract the local colour information between the corresponding pixel and neighbourhood pixels. The experimental results on a sRGB‐to‐ProPhoto dataset have demonstrated that the proposed G‐LGENet outperforms the other excellent GE methods qualitatively and visually.
The competition for shared resources in printing manufacturing system may lead to deadlock, which could cause unnecessary downtime and bring vast economic loss for enterprises. Supervisor is widely studied in the literature to solve the deadlock control problem. With the objective of improving the robustness of reconfigurable printing manufacturing system (RPMS), this paper designs a new Agent-Resource-Workstation (ARW) model with a supervisor. A new reconfiguration of workstation models (NRWMs) is proposed based on ARW model. The supervisors can control reconfiguration behavior of NRWMs by limiting the time and frequency of interaction between workstation models. Then, formal verification is performed by process algebra to show its external behavior. We use the mCRL2 tool to carry out simulation experiments on NRWMs and provide the experimental results. The results show that robust deadlock control policy for the RPMS is secure and effective.
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