The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery

Zhou, Jingjing; Guo, Rui; Sun, Mengtian; Di, Tajiguli Tu; Wang, Shan; Zhai, Jiangyuan; Zhong, Zhao

doi:10.1038/s41598-017-07951-w

Cited by 69 publications

(41 citation statements)

References 30 publications

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“…The texture variable extraction was performed with the grey level co-occurrence matrix (GLCM), which is a method that has showed good results in the estimation of forest variables from spectral data [60]. The metrics of second order (co-occurrence) GLCM (Table 3), associated to three different kernels (3 × 3, 5 × 5, 7 × 7), were calculated from NDVI with the software PCI Geomatics [61].…”

Section: Texture Indicesmentioning

confidence: 99%

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

López-Serrano

Domínguez

Corral-Rivas

et al. 2019

Forests

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An accurate estimation of forests’ aboveground biomass (AGB) is required because of its relevance to the carbon cycle, and because of its economic and ecological importance. The selection of appropriate variables from satellite information and physical variables is important for precise AGB prediction mapping. Because of the complex relationships for AGB prediction, non-parametric machine-learning techniques represent potentially useful techniques for AGB estimation, but their use and comparison in forest remote-sensing applications is still relatively limited. The objective of the present study was to evaluate the performance of automatic learning techniques, support vector regression (SVR) and random forest (RF), to predict the observed AGB (from 318 permanent sampling plots) from the Landsat 8 Landsat 8 Operational Land Imager (OLI) sensor, spectral indexes, texture indexes and physical variables the Sierra Madre Occidental in Mexico. The result showed that the best SVR model explained 80% of the total variance (root mean square error (RMSE) = 8.20 Mg ha−1). The variables that best predicted AGB, in order of importance, were the bands that belong to the region of red and near and middle infrared, and the average temperature. The results show that the SVR technique has a good potential for the estimation of the AGB and that the selection of the model hyperparameters has important implications for optimizing the goodness of fit.

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Section: Texture Indicesmentioning

confidence: 99%

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

López-Serrano

Domínguez

Corral-Rivas

et al. 2019

Forests

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“…5 shows the direction analysis of GLCM with a simple example. Subsequently, GLCM has shown powerful ability on automatic texture discrimination in [36][37][38]. However, it is not an easy job to balance the matrix performance and the window size.…”

Section: ) Co-occurrence Matrixmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

340

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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“…Recently, the literature based on GLCM demonstrates its good performance in many texture analytical tasks [ 30 , 31 , 32 , 33 , 34 ]. In the classical methods based on GLCM, dozens of texture features are calculated from GLCM, and then fused by the feature fusion method [ 34 ].…”

Section: Background and Related Workmentioning

confidence: 99%

High-Precision Detection of Defects of Tire Texture Through X-ray Imaging Based on Local Inverse Difference Moment Features

Zhao

Qin

2018

Sensors

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Automatic defect detection is an important and challenging issue in the tire industrial quality control. As is well known, the production quality of tire is directly related to the vehicle running safety and passenger security. However, it is difficult to inspect the inner structure of tire on the surface. This paper proposes a high-precision detection of defects of tire texture image obtained by X-ray image sensor for tire non-destructive inspection. In this paper, the feature distribution generated by local inverse difference moment (LIDM) features is proposed to be an effective representation of tire X-ray texture image. Further, the defect feature map (DFM) may be constructed by computing the Hausdorff distance between the LIDM feature distributions of original tire image and each sliding image patch. Moreover, DFM may be enhanced to improve the robustness of defect detection algorithm by a background suppression. Finally, an effective defect detection algorithm is proposed to achieve the pixel-level detection of defects with high precision over the enhanced DFM. In addition, the defect detection algorithm is not only robust to the noise in the background, but also has a more powerful capability of handling different shapes of defects. To validate the performance of our proposed method, two kinds of experiments about the defect feature map and defect detection are conducted to demonstrate its good performance. Moreover, a series of comparative analyses demonstrate that the proposed algorithm can accurately detect the defects and outperforms other algorithms in terms of various quantitative metrics.

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The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery

Cited by 69 publications

References 30 publications

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Forests

Automated Visual Defect Detection for Flat Steel Surface: A Survey

High-Precision Detection of Defects of Tire Texture Through X-ray Imaging Based on Local Inverse Difference Moment Features

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