Potential of texture metrics derived from high-resolution PLEIADES satellite data for quantifying aboveground carbon of Kandelia candel mangrove forests in Southeast China

Wang, Min; Cao, Wenzhi; Guan, Qingsong; Wu, Guanghua; Jiang, Chao; Yan, Yongke; Su, Xiaoling

doi:10.1007/s11273-018-9610-2

Cited by 10 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, we carried out vegetation survey and aboveground biomass (AGB) estimation in the two mangrove forests. Detailed information of AGB was described in a previously published article [36] .…”

Section: Methodsmentioning

confidence: 99%

Soil organic carbon storages and bacterial communities along a restored mangrove soil chronosequence in the Jiulong River Estuary: From tidal flats to mangrove afforestation

Wang

Guo

Zhang

et al. 2023

Fundamental Research

Self Cite

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Soil organic carbon storages and bacterial communities along a restored mangrove soil chronosequence in the Jiulong River Estuary: From tidal flats to mangrove afforestation

Wang

Guo

Zhang

et al. 2023

Fundamental Research

Self Cite

View full text Add to dashboard Cite

“…High-level statistics describe the SRE, LRE, GLN, RLN, RP, LGRE, and HGRE values obtained by the GLRLM method. In addition, valuable textural informative features can be obtained from the mammographic image based on LBP, Gabor function and Wavelet transforms [7,8,9].…”

Section: Mammogram Imagesmentioning

confidence: 99%

Extraction of Informative Features in Mammography Images Based on the LBP Method

Abdieva,

Yusupov

2023

Международный журнал теоретических и прикладных вопросов цифров

View full text Add to dashboard Cite

Mammography is crucial for early identification and screening of breast cancer. Mammogram images can identify and classify anomalies and explain breast tissue, as well as other useful information. A technique for obtaining informative signs to lower false positives in the detection of breast tumor areas is investigated in this research work. The key problem in this investigation is differentiating the area of the breast tumor from the area of normal tissue. This issue was resolved by using the LBP approach to extract informative characters from the region of interest (ROI) in the mammography images provided in the international mini-MIAS image dataset.

show abstract

“…9 Afterward, another scientist Gotlieb classified these 14 different features in a set of four different features, that is, texture visual features, correlation measures, entropy measures, and statistical measures. 10 GLCM is widely used in Earth data processing, 11 fabric defect detection, 12 soil moisture estimation, 13 and so on, thus this technique has gained a lot of popularity in remote sensing applications. While calculating GLCM from an input image two factors are considered most important, that is, distance "d" and orientation angle " ".…”

Section: Gray Level Cooccurrence Matrixmentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

View full text Add to dashboard Cite

Texture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land-use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work. K E Y W O R D Sautocorrelation function, gray level cooccurrence matrix, histogram, local binary pattern, texture 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Potential of texture metrics derived from high-resolution PLEIADES satellite data for quantifying aboveground carbon of Kandelia candel mangrove forests in Southeast China

Cited by 10 publications

References 42 publications

Soil organic carbon storages and bacterial communities along a restored mangrove soil chronosequence in the Jiulong River Estuary: From tidal flats to mangrove afforestation

Soil organic carbon storages and bacterial communities along a restored mangrove soil chronosequence in the Jiulong River Estuary: From tidal flats to mangrove afforestation

Extraction of Informative Features in Mammography Images Based on the LBP Method

Study of statistical methods for texture analysis and their modern evolutions

Contact Info

Product

Resources

About