Identification of vegetation under natural gas leakage by spectral index based on feature selection

Pan, Yingyang; Jiang, Jinbao; Liu, Ziwei; Du, Yanjun; Xiong, Kangni

doi:10.1080/01431161.2022.2085068

Cited by 10 publications

(2 citation statements)

References 54 publications

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“…Vegetation indices are combinations of satellite visible and near-infrared bands based on the spectral characteristics of vegetation. They are empirical, simple and effective measures of vegetation conditions [1] . Vegetation indices compress the primary spectral information from different bands into a single vegetation index channel, emphasising the expression of the main information in remote sensing images according to the research objectives.…”

Section: Vegetation Index Featuresmentioning

confidence: 99%

Planting areas extraction of sugarcane using multi-feature fusion method based on Sentinel-2A satellite images

Xiaobin,

Sheng,

Lilu

et al. 2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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In order to accurately identify large-scale plantations, pre-processed Sentinel-2A data from three different time periods were used in this study. The vegetation index features NDVI, BI2 and S2REP were calculated and the optimal texture feature, homogeneity, was selected using principal component analysis and the grey level co-occurrence matrix. A random forest algorithm was then used to construct identification models with different combinations of features, including single time period and single feature, single time period and multiple features, multiple time periods and single feature, and multiple time periods and multiple features. The experiment was conducted in Nansha District, Guangzhou, and the results showed that the fusion of NDVI, BI2, homogeneity and provided the best identification results for sugarcane planting areas, with clear object boundaries and effective control of salt and pepper noise in the images. The overall accuracy reached 0.974 and the kappa coefficient reached 0.931. In the result validation, the identification accuracy for Nansha District, Guangzhou was 81.6%. The experimental errors were also analysed and the method was found to be suitable for determining the area under cultivation in the Pearl River Delta region, providing valuable reference information for the agricultural sector in of , estimating yields and forecasting prices.

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Section: Vegetation Index Featuresmentioning

confidence: 99%

Planting areas extraction of sugarcane using multi-feature fusion method based on Sentinel-2A satellite images

Xiaobin,

Sheng,

Lilu

et al. 2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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“…This algorithm makes it easy to obtain the optimal citrus feature space before model training. Relief F is an extension of the Relief algorithm, which works well for multiclassification issues [26]. The algorithm has high robustness and is appropriate for handling noisy and partial data [27].…”

Section: Selecting Spectral Indicesmentioning

confidence: 99%

Extracting Citrus-Growing Regions by Multiscale UNet Using Sentinel-2 Satellite Imagery

Li,

Liu,

et al. 2023

Remote Sensing

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Citrus is an important commercial crop in many areas. The management and planning of citrus growing can be supported by timely and efficient monitoring of citrus-growing regions. Their complex planting structure and the weather are likely to cause problems for extracting citrus-growing regions from remote sensing images. To accurately extract citrus-growing regions, deep learning is employed, because it has a strong feature representation ability and can obtain rich semantic information. A novel model for extracting citrus-growing regions by UNet that incorporates an image pyramid structure is proposed on the basis of the Sentinel-2 satellite imagery. A pyramid-structured encoder, a decoder, and multiscale skip connections are the three main components of the model. Additionally, atrous spatial pyramid pooling is used to prevent information loss and improve the ability to learn spatial features. The experimental results show that the proposed model has the best performance, with the precision, the intersection over union, the recall, and the F1-score reaching 88.96%, 73.22%, 80.55%, and 84.54%, respectively. The extracted citrus-growing regions have regular boundaries and complete parcels. Furthermore, the proposed model has greater overall accuracy, kappa, producer accuracy, and user accuracy than the object-oriented random forest algorithm that is widely applied in various fields. Overall, the proposed method shows a better generalization ability, higher robustness, greater accuracy, and less fragmented extraction results. This research can support the rapid and accurate mapping of large-scale citrus-growing regions.

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Detecting natural gas storage microleakage based on K-means clustering under constraint of Jeffries-Matusita distance criterion using mobile LiDAR data

Wang,

Li,

Jiang

et al. 2024

Journal of Environmental Management

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Identification of vegetation under natural gas leakage by spectral index based on feature selection

Cited by 10 publications

References 54 publications

Planting areas extraction of sugarcane using multi-feature fusion method based on Sentinel-2A satellite images

Planting areas extraction of sugarcane using multi-feature fusion method based on Sentinel-2A satellite images

Extracting Citrus-Growing Regions by Multiscale UNet Using Sentinel-2 Satellite Imagery

Detecting natural gas storage microleakage based on K-means clustering under constraint of Jeffries-Matusita distance criterion using mobile LiDAR data

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