Estimating Change Percentage in Texture Developed by the Water Turndown of Bolivia's Lake Poopo

Shakya, Amit Kumar; Ramola, Ayushman; Sakshi, .; Kandwal, Akhilesh

doi:10.1109/icace.2018.8687003

Cited by 5 publications

(1 citation statement)

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“…Texture provides a quantitative assessment of spatial information which is widely used in satellite remote sensing. 35 The successful classification of radar images based on GLCM features is presented in Reference 36. Sea ice is classified by Holmes et al 28 and urban areas are classified by Baraldi and Panniggiani 31 using GLCM.…”

Section: Difference Entropymentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

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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.

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Section: Difference Entropymentioning

confidence: 99%