Object-Based Attention Mechanism for Color Calibration of UAV Remote Sensing Images in Precision Agriculture

Huang, Huasheng; Tang, Yu; Tan, Zhiping; Zhuang, Jiajun; Hou, Chao; Chen, Weizhao; Ren, Jinchang

doi:10.1109/tgrs.2022.3224580

Cited by 14 publications

(3 citation statements)

References 34 publications

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“…Therefore, Ω 4 × Ω 5 is a compact set on R 6 and Ω 4 × Ω 6 is a compact set on R 8 . Furthermore, η 3 , η 4 are bounded on Ω 4 × Ω 5 and Ω 4 × Ω 6 , respectively.…”

Section: Stability Analysismentioning

confidence: 99%

“…In recent years, drones have garnered increasing attention, with their applications expanding significantly across both civilian and military domains. In the civilian sector, they are frequently employed for tourism photography [1], formation flight performances [2,3], air quality monitoring [4], forest fire surveillance [5], remote sensing imagery for precision agriculture [6], and other civilian applications. In the military realm, drones serve as military relay networks [7] and are also utilized in swarming combat systems [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

Zhu,

Lv,

Sun

et al. 2024

Drones

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An adaptive dynamic surface trajectory tracking control method based on the Nussbaum function is proposed for a class of quadrotor UAVs encountering unknown external disturbances and unidentified nonlinearities. By transforming controller expressions into numerical solutions, the challenge of overly complex controller design expressions is addressed, simplifying the overall controller design process and enhancing the efficiency of simulation programs. Additionally, an adaptive controller based on Nussbaum gain is introduced to effectively resolve actuator saturation issues. This approach mitigates complexities associated with traditional control design and ensures smooth operation of the quadrotor UAVs. The proposed methodology offers promising prospects for enhancing the robustness and performance of quadrotor UAVs under uncertain operating conditions. Finally, to validate the effectiveness of the proposed control scheme, a hardware-in-the-loop experimental setup is constructed. The dynamic model of the quadrotor UAVs and the proposed controller scheme are implemented on the Rapid Control Prototype (RCP) and Real-Time Simulator (RTS), respectively. This facilitates a semi-physical simulation experiment, providing a basis for the subsequent application of the control scheme to actual aerial vehicles. The concluding experimental results affirm the effectiveness of the proposed control scheme and highlight its potential for practical applications.

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“…Therefore, Ω 4 × Ω 5 is a compact set on R 6 and Ω 4 × Ω 6 is a compact set on R 8 . Furthermore, η 3 , η 4 are bounded on Ω 4 × Ω 5 and Ω 4 × Ω 6 , respectively.…”

Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

Zhu,

Lv,

Sun

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…Unmanned aerial vehicles (UAVs) have been widely used in various fields, such as transportation [1], agriculture [2], environmental monitoring [3,4], and military affairs [5], due to their flexible operation and low cost. Regardless of * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Improve exploration in deep reinforcement learning for UAV path planning using state and action entropy

Lv,

Chen,

et al. 2024

Meas. Sci. Technol.

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Despite being a widely adopted development framework for unmanned aerial vehicle (UAV), deep reinforcement learning (DRL) is often considered sample inefficient. Particularly, UAV struggles to fully explore the state and action space in environments with sparse rewards. While some exploration algorithms have been proposed to overcome the challenge of sparse rewards, they are not specifically tailored for UAV platform. Consequently, applying those algorithms to UAV path planning may lead to problems such as unstable training processes and neglect of action space comprehension, possibly causing negative impacts on the path planning results. To address the problem of sparse rewards in UAV path planning, we propose an information-theoretic exploration algorithm, Entropy Explorer (EE), specifically for UAV platform. The proposed EE generates intrinsic rewards based on state entropy and action entropy to compensate for the scarcity of extrinsic rewards. To further improve sampling efficiency, a framework integrating EE and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms is proposed. Finally, the TD3-EE algorithm is tested in Airsim and compared against benchmarking algorithms. The simulation outcomes manifest that TD3-EE effectively stimulates the UAV to comprehensively explore both state and action spaces, thereby attaining superior performance compared to the benchmark algorithms in the realm of path planning.

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Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

Xu,

Jin,

Guo

2024

International Journal of Remote Sensing

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Object-Based Attention Mechanism for Color Calibration of UAV Remote Sensing Images in Precision Agriculture

Abstract: Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture. IEEE transactions on geoscience and remote sensing [online], 60, article number 4416013.

Cited by 14 publications

References 34 publications

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

Improve exploration in deep reinforcement learning for UAV path planning using state and action entropy

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

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