Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation

Guarnieri, Andrea Monti; Manzoni, Marco; Giudici, Davide; Recchia, Andrea; Tebaldini, Stefano

doi:10.3390/rs12162545

Cited by 21 publications

(11 citation statements)

References 35 publications

(109 reference statements)

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“…Step 1: according to the relevant influencing factors in the development process of e-government, select the appropriate indicator data of e-government performance evaluation, use relevant algorithms to conduct standardized processing of indicator data [28], and then process the processed indicator data X 1 , X 2 , X 3 , ..., X n as the input value is of the PSO-BP neural network at the input level.…”

Section: Online Teaching Quality Evaluation Model Based On Hierarchical Pso-bp Neural Networkmentioning

confidence: 99%

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Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Jiang

Wang

2020

Complexity

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In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process (AHP) and particle swarm optimization BP neural network (PSO-BP) is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network.

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Section: Online Teaching Quality Evaluation Model Based On Hierarchical Pso-bp Neural Networkmentioning

confidence: 99%

“…Multiobjective Evolution of Fuzzy Rough Neural Network has been researched [26,27]. A minimum center distance rule activation method and Target Decorrelation in SAR are discussed [28,29].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Jiang

Wang

2020

Complexity

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“…With the recent availability of S1B data, the experiment conducted in [12] can be extended by reducing the temporal baseline to six days, which would reduce the temporal decorrelation between the images [4,18]. This should lead to higher coherence values for post-mowing short vegetation conditions, thus enhancing the signatures of the farming events in the coherence time series.…”

Section: Introductionmentioning

confidence: 99%

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

et al. 2020

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Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.

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“…For instance, authors of [3] showed that the short-term variable reflectivity induced by wind, typical of vegetated scenarios, can be theoretically characterized and modeled. In [4] different theoretical models for the temporal decorrelation of GBSAR images in vegetated scenarios are examined. For the short-term signal variability, a model with dependence on the wind speed is presented and validated for experimental data acquired in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Temporal Coherence Estimators for GBSAR

Beni

Miccinesi²,

Michelini³

et al. 2022

Remote Sensing

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Many Ground-Based Synthetic Aperture Radar (GBSAR) applications demand preliminary analysis to select areas with high-quality signal. That is, areas in which the phase can be processed to extract the desired information. The interferometric coherence and the amplitude dispersion index are important tools widely used in the literature to assess the quality of GBSAR images. So far, no direct relation has been found between the two. Indeed, they are parameters of different natures: amplitude dispersion index is calculated with only amplitude values, while coherence provides information also on the signal phase. The purpose of this article is to find a relation between the two parameters. Indeed, the amplitude dispersion index provides some practical advantages if compared to coherence estimators, especially to perform fast preliminary analysis. In this article, a theoretical relation between amplitude dispersion index and coherence is retrieved. GBSAR measurements acquired in different scenarios, at different working frequencies are presented and used to validate such a relation.

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Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation

Cited by 21 publications

References 35 publications

Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Optimization of Online Teaching Quality Evaluation Model Based on Hierarchical PSO-BP Neural Network

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

Temporal Coherence Estimators for GBSAR

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