Evaluation of the Segmentation of Remote Sensing Images

Kazakeviciute-Januskeviciene, Giruta; Janusonis, Edgaras; Baušys, Romualdas

doi:10.1109/estream53087.2021.9431459

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…With the deepening of research work, in recent years, the performance evaluation research carried out by scholars has gradually focused on satellite structure, power supply, attitude control and other subsystems. Kazakeviciute et al examined the effectiveness of remote sensing image segmentation for remote sensing satellites, and evaluated the performance of remote sensing satellite images by determining the correlation between the subjective and objective segmentation quality indexes [11]. Based on fuzzy mathematics and hierarchical analysis, Yang Jun established an index system for evaluating the performance of satellite navigation systems and illustrated the evaluation process and methodology with examples [12].…”

Section: Introductionmentioning

confidence: 99%

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

Zhang,

Yang,

Cheng

et al. 2024

Applied Sciences

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The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.

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Section: Introductionmentioning

confidence: 99%

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

Zhang,

Yang,

Cheng

et al. 2024

Applied Sciences

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Deep Learning-Based Infrared Image Segmentation for Aircraft Honeycomb Water Ingress Detection

Fei,

Zuo,

Wang

et al. 2024

Aerospace

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The presence of water accumulation on aircraft surfaces constitutes a considerable hazard to both performance and safety, necessitating vigilant inspection and maintenance protocols. In this study, we introduce an innovative semantic segmentation model, grounded in deep learning principles, for the precise identification and delineation of water accumulation areas within infrared images of aircraft exteriors. Our proposed model harnesses the robust features of ResNet, serving as the foundational architecture for U-Net, thereby augmenting the model’s capacity for comprehensive feature characterization. The incorporation of channel attention mechanisms, spatial attention mechanisms, and depthwise separable convolution further refines the network structure, contributing to enhanced segmentation performance. Through rigorous experimentation, our model surpasses existing benchmarks, yielding a commendable 22.44% reduction in computational effort and a substantial 38.89% reduction in parameter count. The model’s outstanding performance is particularly noteworthy, registering a 92.67% mean intersection over union and a 97.97% mean pixel accuracy. The hallmark of our innovation lies in the model’s efficacy in the precise detection and segmentation of water accumulation areas on aircraft skin. Beyond this, our approach holds promise for addressing analogous challenges in aviation and related domains. The enumeration of specific quantitative outcomes underscores the superior efficacy of our model, rendering it a compelling solution for precise detection and segmentation tasks. The demonstrated reductions in computational effort and parameter count underscore the model’s efficiency, fortifying its relevance in broader contexts.

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Evaluation of the Segmentation of Remote Sensing Images

Cited by 2 publications

References 13 publications

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode

Deep Learning-Based Infrared Image Segmentation for Aircraft Honeycomb Water Ingress Detection

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