Histogram-Based Autoadaptive Filter for Destriping NDVI Imagery Acquired by UAV-Loaded Multispectral Camera

Lin, Jin‐Ming; Zuo, Hang; Ye, Yaya; Liu, Xiaohan

doi:10.1109/lgrs.2018.2877728

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…It can be observed from the maize yield distribution map (Figure 8) that there is slight stripe noise distribution on the UAV route, and this phenomenon also affects the accuracy of the data simulation. Lin et al (2018) used histogram‐based adaptive filtering to effectively reduce streak noise in UAV‐acquired NDVI image data, but it also seems to be affected in the area of maximums.…”

Section: Discussionmentioning

confidence: 99%

“…Ultra-high-resolution RS images can be obtained by UAV and the temporal resolution becomes manageable, but the multispectral sensors have a limited range of spectral band information collection, resulting in the extracted VIs that do not fully reflect the crop growth state, thus causing a lack of multiband vegetation information. In addition, UAVs are affected by flight conditions and cannot guarantee stable flight during the mission, which leads to the error of the feature image elements and makes the inversion of the surface crop growth state inaccurate (Lin et al, 2018). It can be observed from the maize yield distribution map (Figure 8) that there is slight stripe noise distribution on the UAV route, and this phenomenon also affects the accuracy of the data simulation.…”

Section: Uncertainties and Future Workmentioning

confidence: 99%

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Forecasting spring maize yield using vegetation indices and crop phenology metrics from UAV observations

Bao,

Li,

et al. 2023

Food and Energy Security

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Early and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient‐boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield (R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R2 and lowest RMSE of 0.894, 1.238 × 103 kg ha−1, respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R2, RMSE, and MAE of 0.892, 1.189 × 103 kg ha−1, and 9.150 × 102 kg ha−1, respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale.

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Section: Discussionmentioning

confidence: 99%

Section: Uncertainties and Future Workmentioning

confidence: 99%

Forecasting spring maize yield using vegetation indices and crop phenology metrics from UAV observations

Bao,

Li,

et al. 2023

Food and Energy Security

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“…Furthermore, UAVs are indispensable for inspecting agricultural fields [30][31][32][33][34]. Many individuals employ algorithms and models to estimate NDVI (Normalized Difference Vegetation Index) using images from multispectral cameras and aerial platforms equipped with multispectral NDVI imaging systems [35,36]. Some algorithms are designed for route planning or precise mapping through UAVs [37,38].…”

Section: Related Workmentioning

confidence: 99%

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Strzępek,

Salach,

Trybus

et al. 2023

Sensors

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This article presents an integrated system that uses the capabilities of unmanned aerial vehicles (UAVs) to perform a comprehensive crop analysis, combining qualitative and quantitative evaluations for efficient agricultural management. A convolutional neural network-based model, Detectron2, serves as the foundation for detecting and segmenting objects of interest in acquired aerial images. This model was trained on a dataset prepared using the COCO format, which features a variety of annotated objects. The system architecture comprises a frontend and a backend component. The frontend facilitates user interaction and annotation of objects on multispectral images. The backend involves image loading, project management, polygon handling, and multispectral image processing. For qualitative analysis, users can delineate regions of interest using polygons, which are then subjected to analysis using the Normalized Difference Vegetation Index (NDVI) or Optimized Soil Adjusted Vegetation Index (OSAVI). For quantitative analysis, the system deploys a pre-trained model capable of object detection, allowing for the counting and localization of specific objects, with a focus on young lettuce crops. The prediction quality of the model has been calculated using the AP (Average Precision) metric. The trained neural network exhibited robust performance in detecting objects, even within small images.

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“…In this paper, we focus on the destriping task in the Fourier domain for TIR images. Early Fourier domain methods remove stripe noise by using simple 1D filters [36,37], and some newly related methods design adaptive 2D filters to accomplish the goal [45,46]. However, as many literatures pointed out, on the one hand, these naive filtering approaches have limited ability to handle stripes when faced with manifold real cases and most of them are in fact only available for periodic stripes; on the other hand, the methods may cause the so-called ringing artifact owing to the high discontinuity of image intensity and the blur of useful structure details that have the same frequencies as stripes.…”

Section: Motivationmentioning

confidence: 99%

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

2020

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Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method.

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Histogram-Based Autoadaptive Filter for Destriping NDVI Imagery Acquired by UAV-Loaded Multispectral Camera

Cited by 10 publications

References 19 publications

Forecasting spring maize yield using vegetation indices and crop phenology metrics from UAV observations

Forecasting spring maize yield using vegetation indices and crop phenology metrics from UAV observations

Quantitative and Qualitative Analysis of Agricultural Fields Based on Aerial Multispectral Images Using Neural Networks

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

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