An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters

Baraldi, A.; Parmiggiani, F.

doi:10.1109/36.377929

Cited by 445 publications

(197 citation statements)

References 12 publications

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“…It is a way of extracting second-order statistical texture features, while the spectral derivatives can be considered first-order features, as they do not consider pixel neighbour relationships. GLCM was developed by Haralick, Shanmugam, and Dinstein [31], and has commonly been applied in remote sensing studies [15,17,19,22,32,33]. Ten common textural attributes (contrast, dissimilarity, homogeneity, second moment, energy, max probability, entropy, average, variance, and correlation) were computed using the Sentinel Application Platform (SNAP; http://step.esa.int/main/toolboxes/snap) commissioned by the European Space Agency (ESA).…”

Section: Textural Metricsmentioning

confidence: 99%

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Persson

2016

Remote Sensing

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Abstract:In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 × 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey's mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m 2 ·ha −1 (22%) for basal area, 66 m 3 ·ha −1 (27%) for stem volume, and 26 tons·ha −1 (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned.

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Section: Textural Metricsmentioning

confidence: 99%

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Persson

2016

Remote Sensing

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“…Due to its simplicity and efficiency, this method became very popular in the classification of textures. According to [5], among the 14 statistics originally proposed, only 6 have a higher relevance: the second angular momentum, contrast, correlation, entropy, variance, and homogeneity. These six features are used here in the present study.…”

Section: Haralick's Descriptorsmentioning

confidence: 99%

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM

et al. 2014

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Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses.

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“…These statistics are computed in all four directions and with distances d = {1, 2, 3, 4, 5}. A detailed description of these statistics can be found in [11], [12].…”

Section: Texture Analysismentioning

confidence: 99%

Extracting Texture Features for Time Series Classification

Souza

Silva

Batista

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-Time series are present in many pattern recognition applications related to medicine, biology, astronomy, economy, and others. In particular, the classification task has attracted much attention from a large number of researchers. In such a task, empirical researches has shown that the 1-Nearest Neighbor rule with a distance measure in time domain usually performs well in a variety of application domains. However, certain time series features are not evident in time domain. A classical example is the classification of sound, in which representative features are usually present in the frequency domain. For these applications, an alternative representation is necessary. In this work we investigate the use of recurrence plots as data representation for time series classification. This representation has well-defined visual texture patterns and their graphical nature exposes hidden patterns and structural changes in data. Therefore, we propose a method capable of extracting texture features from this graphical representation, and use those features to classify time series data. We use traditional methods such as Grey Level Co-occurrence Matrix and Local Binary Patterns, which have shown good results in texture classification. In a comprehensible experimental evaluation, we show that our method outperforms the state-ofthe-art methods for time series classification.

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An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters

Cited by 445 publications

References 12 publications

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM

Extracting Texture Features for Time Series Classification

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