Superpixel segmentation and machine learning classification algorithm for cloud detection in remote‐sensing images

Shi, Yueting; Wang, Weijiang; Gong, Qishu; Li, Dingyi

doi:10.1049/joe.2019.0240

Cited by 4 publications

(2 citation statements)

References 7 publications

(6 reference statements)

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“…In order to reduce the influence of soil and shadow noise and improve the accuracy of the final quality parameter monitoring results, this study used the EXG index to effectively distinguish the background of green vegetation and soil for image enhancement [31,[79][80][81][82]. The Ostu method was used for image segmentation [32,[83][84][85][86] to enable the effective extraction of the tea areas from the original image that contains other features. In comparison, the predicted results of the model using the EO sampling method were more accurate than the models using the G and M sampling methods.…”

Section: Ground Multispectral Imagesmentioning

confidence: 99%

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

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Section: Ground Multispectral Imagesmentioning

confidence: 99%

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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“…There is a significant value on information extraction of remote sensing images. Remote sensing target detection [5][6][7] has important and extensive applications in military, navigation, salvage, and other aspects, which requires high speed and accuracy for target detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

2020

Sensors

100

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Remote sensing targets have different dimensions, and they have the characteristics of dense distribution and a complex background. This makes remote sensing target detection difficult. With the aim at detecting remote sensing targets at different scales, a new You Only Look Once (YOLO)-V3-based model was proposed. YOLO-V3 is a new version of YOLO. Aiming at the defect of poor performance of YOLO-V3 in detecting remote sensing targets, we adopted DenseNet (Densely Connected Network) to enhance feature extraction capability. Moreover, the detection scales were increased to four based on the original YOLO-V3. The experiment on RSOD (Remote Sensing Object Detection) dataset and UCS-AOD (Dataset of Object Detection in Aerial Images) dataset showed that our approach performed better than Faster-RCNN, SSD (Single Shot Multibox Detector), YOLO-V3, and YOLO-V3 tiny in terms of accuracy. Compared with original YOLO-V3, the mAP (mean Average Precision) of our approach increased from 77.10% to 88.73% in the RSOD dataset. In particular, the mAP of detecting targets like aircrafts, which are mainly made up of small targets increased by 12.12%. In addition, the detection speed was not significantly reduced. Generally speaking, our approach achieved higher accuracy and gave considerations to real-time performance simultaneously for remote sensing target detection.

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An Extensive Survey on Superpixel Segmentation: A Research Perspective

Prakash

Kumar

2023

Arch Computat Methods Eng

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Superpixel segmentation and machine learning classification algorithm for cloud detection in remote‐sensing images

Cited by 4 publications

References 7 publications

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

An Extensive Survey on Superpixel Segmentation: A Research Perspective

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