A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Xin, Zhang; Cui, Jintian; Wang, Weisheng; Lin, Ching Hsuan

doi:10.3390/s17071474

Cited by 185 publications

(120 citation statements)

References 22 publications

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“…These measures quantify differences in the grey levels within a local window [47]. In this study, the window size was set to (5 × 5) pixels, as suggested by Zhang et al [48]. The GLDV texture measures employed were contrast, entropy, and mean, while correlation and homogeneity were selected from the GLCM analyses.…”

Section: Index Equationmentioning

confidence: 99%

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

et al. 2019

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S.M.alfieri-1@tudelft.nl (S.M.A.); M.Menenti@tudelft.nl (M.M.)Abstract: Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polari...

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Section: Index Equationmentioning

confidence: 99%

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

et al. 2019

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“…However, the study did not provide universal testimony for the significance of these advantages for the accuracy of measurements . Extensive research is carried out on non‐contact‐type assessment of surface roughness parameters using machine vision system and artificial intelligence technology, which include methods such as laser speckle, light scattering, and optical interference . Pontes et al proposed the technique called multilayer perceptron (MLP) network architecture, which considerably reduces errors in predicting surface roughness parameters of machined components compared with currently used techniques.…”

Section: Introductionmentioning

confidence: 99%

“…14 Extensive research is carried out on non-contact-type assessment of surface roughness parameters using machine vision system and artificial intelligence technology, which include methods such as laser speckle, light scattering, and optical interference. [15][16][17][18][19] Pontes et al 20 proposed the technique called multilayer perceptron (MLP) network architecture, which considerably reduces errors in predicting surface roughness parameters of machined components compared with currently used techniques. Huaian et al presented a new methodology to assess surface roughness that uses uniform texture direction without any primary necessities, which defeats the present issues such as limited range, complex calculations, and so on.…”

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confidence: 99%

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray‐level co‐occurrence matrix and correlated with different surface roughness parameters recorded by a contact‐type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of −9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement.

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“…The statistical parameters applied in this study were angular second moment (ASM), contrast, correlation, and entropy, with better texture feature extraction in remote sensing [52]. ASM reflects the regularity and uniformity of image distribution; contrast reflects the depth and smoothness of the image texture structure; correlation reflects the similarity of the image texture in the horizontal or vertical direction, and entropy is a measure of image information that reflects the complexity of the texture distribution [52]; the mean pixel values, and the variance values, which are the main statistical values. Next, the GLCM, mean, and variance were obtained only for the R (Band 3), G (Band 2), and B (Band 1) values to be modeled for visual interpretation.…”

Section: Radiometric Normalization Using Random Forest Regressionmentioning

confidence: 99%

“…The gray-level co-occurrence matrix (GLCM) data were obtained using statistical values between a given pixel and the neighboring pixels, which showed the spatial characteristics of the pixels as a texture feature [51]. The statistical parameters applied in this study were angular second moment (ASM), contrast, correlation, and entropy, with better texture feature extraction in remote sensing [52]. ASM reflects the regularity and uniformity of image distribution; contrast reflects the depth and smoothness of the image texture structure; correlation reflects the similarity of the image texture in the horizontal or vertical direction, and entropy is a measure of image information that reflects the complexity of the texture distribution [52]; the mean pixel values, and the variance values, which are the main statistical values.…”

Section: Radiometric Normalization Using Random Forest Regressionmentioning

confidence: 99%

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Seo

Kim

et al. 2017

Remote Sensing

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Abstract:Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth's surface by using satellite remote sensing imagery. There is a need to maintain the homogeneity of radiometric and phenological conditions to ensure accuracy in change detection, but images to assess long-term changes in time-series data that satisfy such conditions are difficult to obtain. For this reason, image normalization is essential. In particular, the normalizing compositive conditions require nonlinear modeling, and random forest (RF) techniques can be utilized for this normalization. This study employed Landsat-5 Thematic Mapper satellite images with temporal, radiometric and phenological differences, and obtained Radiometric Control Set Samples by selecting no-change pixels between the subject image and reference image using scattergrams. In the obtained no-change regions, RF regression was modeled, and normalized images were obtained. Next, normalization performance was evaluated by comparing the results against the following conventional linear regression methods: mean-standard deviation regression, simple regression, and no-change regression. The normalization performance of RF regression was much higher. In addition, for an additional usefulness evaluation in normalization, the normalization performance was compared with other nonlinear ensemble regressions, i.e. Adaptive Boosting regression and Stochastic Gradient Boosting regression, which confirmed that the normalization performance of RF regression was significantly higher. In other words, it was found to be highly useful for normalization when compared to other nonlinear ensemble regressions. Finally, as a result of performing change detection, normalized subject images generated by RF regression showed the highest accuracy, which indicated that the proposed method (where the image was normalized using RF regression) may be useful in change detection between multi-temporal image datasets.

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A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Cited by 185 publications

References 22 publications

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

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