This paper investigates a non-fragile robust security control strategy for remote motors, based on a dynamic threshold cryptographic detector. This strategy aims to protect system performance against stealthy false data injection (FDI) attacks and to effectively minimize the impact of controller jitter. First, a stealthy FDI attack is designed to bypass the conventional$\chi ^{2}$detector and degrade system performance. The stealthiness and destructiveness of the attack are demonstrated. Next, to counter the stealthy FDI attack, a dynamic threshold cryptographic detector is proposed. This detector addresses the stealthiness of the attack and enhances robustness by incorporating a time-varying nonlinear function and a dynamic threshold detection strategy. Furthermore, a non-fragile robust security control strategy is introduced to prevent these attacks and mitigate the problem of controller perturbations. The stability of this strategy is proven using Lyapunov theory. Finally, the effectiveness of the proposed security control strategy is validated through numerical and semi-physical simulations.

This article investigates the secure control of a class of servo DC motors in the presence of input–output disturbances and DoS attacks. A multichannel observer-based switchingH∞ control strategy is proposed and a dynamic event triggering mechanism (DETM) is designed to save network resources. First, a mathematical model of servo DC motor containing input–output disturbances is developed and discretized to make it more suitable for computer control. Then, a state observer and a multichannel transmission strategy based on Markov theory are designed in order to obtain the accurate knowledge of disturbed system and transmit it to the remote controller under DoS attack. Third, observer-based state feedback switchingH∞ control strategy is proposed and the stability is demonstrated. Furthermore, the DETM is presented to reduce the occupation of network resources by introducing dynamic trigger variable. Finally, the performance of the characterized control strategy is verified by a numerical simulation and a semi-physical simulation.

Unmanned aerial vehicle (UAV) offers an unprecedented observing potential with ultrahigh spatial and temporal resolutions and high flexibility. However, it remains difficult to solve the radiometric inconsistency of multiflight UAV imagery. This study proposed a relative radiometric correction method for multiflight UAV images based on concurrent satellite imagery (MACA) consisting of two steps, i.e., cross-sensor spectral fitting (CSF) and fine-resolution spectral calibration (FSC). In CSF, relationships of multiflight UAV reflectance and the concurrent satellite reflectance were established for generating the fine-resolution reference imagery. Subsequently, a relative radiometric correction model was constructed in the FSC step to correct multiflight UAV imagery. The performance of MACA was evaluated using multiflight UAV datasets acquired on six cropland sites and a concurrent Sentinel-2 image. Compared with four typical or state-of-the-art relative correction methods, the correction using MACA yielded better consistency between UAV and Sentinel-2 data, regardless of individual spectral bands ($\text{R}^{2} =0.79$–0.86, root mean square error (RMSE)$=0.004$–0.019) or vegetation indices (VIs) ($\text{R}^{2} =0.80$–0.86, RMSE$=0.024$–0.054). Moreover, the prediction of plant nitrogen accumulation (PNA) based on the MACA-corrected UAV data had the highest accuracy and showed the spatial variation most significantly within and between fields for all sites. The results demonstrated that MACA was more robust in reducing spectral mismatch across sensors and eliminating the subjective error of pseudo-invariant features (PIFs) selection. MACA has the potential to be used to cross-calibrate multisensor data into a consistent standard, which will benefit multisensor synergies.

The city of Beijing is going through a rapid developing period, both in the suburbs and inside the old city. To detect the buildings developed in and around the city at present, Landsat-7 ETM+ and SPOT panchromatic imagery of 2001 were collected. To make use of higher spatial resolution of the SPOT panchromatic imagery and higher spectral resolution of the ETM+ imagery, the two data sources were fused. To reduce the confusion between urban built-up areas and non built-up areas, an unsupervised classification was performed first. Agricultural and large urban green and open areas such as parks and lakes were separated from other built-up areas. Major roads were mapped from ETM+ band8. Buildings were then extracted from the SPOT PAN imagery within the built-up areas using a set of proposed high-pass filters. Traditional Chinese-style houses, which are small in size and unidentifiable individually from the satellite imagery, were not detected. Our result shows that many medium-to-large buildings of regular arrangement can be mapped from the fused dataset. The buildings extracted from the satellite imagery can provide useful information for urban change detection and environmental management.