Comparison of pixel- and object-based image analysis for tea plantation mapping using hyperspectral Gaofen-5 and synthetic aperture radar data

Chen, Yujia; Tian, Senlin

doi:10.1117/1.jrs.14.044516

Cited by 3 publications

(5 citation statements)

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“…Several studies have utilized high spatiotemporal-resolution multispectral imagery to evaluate the spatial distribution and area of tea plantations, including Li et al [83] , Huang et al [84] , and Dihkan et al [85] Various satellite resources, such as Sentinel-2 [3] and WorldView-2 [86] , have been used to examine the distribution and extraction of tea plantations. In addition, remote sensing technologies such as Landsat [87] , SAR [88] , Lidar [89] , and hyperspectral data [90] are also utilized for tea garden classification. When applying remote sensing technology to tea plantation extraction, color, texture, spectral, and terrain features are considered, including NDVI [86,91] , MNDVI [85] , EVI [88] , MNDWI [88] , LSWI [88] , GLCM texture [86] , Gabor texture [86] , DEM [88] .…”

Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

“…In addition, remote sensing technologies such as Landsat [87] , SAR [88] , Lidar [89] , and hyperspectral data [90] are also utilized for tea garden classification. When applying remote sensing technology to tea plantation extraction, color, texture, spectral, and terrain features are considered, including NDVI [86,91] , MNDVI [85] , EVI [88] , MNDWI [88] , LSWI [88] , GLCM texture [86] , Gabor texture [86] , DEM [88] . Furthermore, machine learning or deep learning methods coupled with multiple remote sensing technologies have been used to differentiate tea cultivars from other vegetation.…”

Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

“…SAR data PALSAR-1 10 Contributions to cartography, precise regional land cover assessment, disaster monitoring, and resource surveys. [88] Google Earth Google earth image 0.27-71 000 Fast access speed, reliable data, high coverage rate, and promptness. [76] Note: MODIS: Moderate-resolution imaging spectroradiometer, GF: Gaofen, LiDAR: Light detection and ranging, SAR: Synthetic aperture radar, PALSAR: Phased array L-band synthetic aperture radar.…”

Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

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Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Cao,

Zhao,

et al. 2024

International Journal of Agricultural and Biological Engineering

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The tea plant is a valuable and evergreen crop that is extensively cultivated in China and many other countries. Currently, there is growing research interest in this plant. For the tea industry, it is crucial to develop rapid and non-invasive methods to evaluate tea plants in their natural environment. This article provides a comprehensive overview of non-invasive sensing techniques used for in-situ detection of tea plants. The topics covered include leaf, canopy, and field-level assessments, as well as statistical analysis techniques and characteristics specific to the research. Non-invasive testing technology is primarily used for monitoring and predicting tea pests and diseases, monitoring quality, and nutrients, determining tenderness and grade, identifying tea plant varieties, automatically detecting, and identifying tea buds, monitoring tea plant growth, and extracting tea garden areas through remote sensing. It also helps to evaluate planting suitability, assess disasters, and estimate yields. Additionally, the article examines the challenges and prospects of emerging techniques aimed at resolving the in-situ detection problem for tea plants. It can assist researchers and producers in comprehensively understanding the tea environment, quality characteristics, and growth process, thereby enhancing tea production quality, and fostering tea industry development.

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Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

Section: Tea Plantations Extraction and Dynamic Changes Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Cao,

Zhao,

et al. 2024

International Journal of Agricultural and Biological Engineering

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“…Due to the Hughes effect, hyperspectral images are not directly used in land cover classification [35]. Improving on previous studies [20,21], we selected 3 kinds of features including Minimum Noise Fraction (MNF), band, and index features.…”

Section: Spectral Featurementioning

confidence: 99%

“…Specifically, much researchers has already combined hyperspectral, multispectral, SAR data, and the like together to identify vegetation and evaluate wetlands and soil quality; and made great progress in these research pieces. Additionally, the overall accuracy of the classification has been significantly improved [20][21][22]. The current focus of multi-source imagery classification is to optimally select feature combination [23], as too many features probably weaken the performance of the classifier and lead to unfavorable low accuracy and inefficiency [24].…”

Section: Introductionmentioning

confidence: 99%

Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Zhou

Tian

Chen

et al. 2022

Sensors

Self Cite

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Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.

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Large-area, 1964 land cover classifications of Corona spy satellite imagery for the Caucasus Mountains

Rizayeva

Niţă

Radeloff

2023

Remote Sensing of Environment

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Comparison of pixel- and object-based image analysis for tea plantation mapping using hyperspectral Gaofen-5 and synthetic aperture radar data

Cited by 3 publications

References 72 publications

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Large-area, 1964 land cover classifications of Corona spy satellite imagery for the Caucasus Mountains

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