Gray Level Co-occurrence Matrix Texture Research Articles

Application of Two-Dimensional Entropy Measures to Detect the Radiographic Signs of Tooth Resorption and Hypercementosis in an Equine Model.

Dental disorders are a serious health problem in equine medicine, their early recognition benefits the long-term general health of the horse. Most of the initial signs of Equine Odontoclastic Tooth Resorption and Hypercementosis (EOTRH) syndrome concern the alveolar aspect of the teeth, thus, the need for early recognition radiographic imaging. This study is aimed to evaluate the applicability of entropy measures to quantify the radiological signs of tooth resorption and hypercementosis as well as to enhance radiographic image quality in order to facilitate the identification of the signs of EOTRH syndrome. A detailed examination of the oral cavity was performed in eighty horses. Each evaluated incisor tooth was assigned to one of four grade-related EOTRH groups (0-3). Radiographs of the incisor teeth were taken and digitally processed. For each radiograph, two-dimensional sample (SampEn2D), fuzzy (FuzzEn2D), permutation (PermEn2D), dispersion (DispEn2D), and distribution (DistEn2D) entropies were measured after image filtering was performed using Normalize, Median, and LaplacianSharpening filters. Moreover, the similarities between entropy measures and selected Gray-Level Co-occurrence Matrix (GLCM) texture features were investigated. Among the 15 returned measures, DistEn2D was EOTRH grade-related. Moreover, DistEn2D extracted after Normalize filtering was the most informative. The EOTRH grade-related similarity between DistEn2D and Difference Entropy (GLCM) confirms the higher irregularity and complexity of incisor teeth radiographs in advanced EOTRH syndrome, demonstrating the greatest sensitivity (0.50) and specificity (0.95) of EOTRH 3 group detection. An application of DistEn2D to Normalize filtered incisor teeth radiographs enables the identification of the radiological signs of advanced EOTRH with higher accuracy than the previously used entropy-related GLCM texture features.

Using gray level co-occurrence matrix texture features extracted from dual time point PET/CT images to diagnose malignant FDG-avid solitary pulmonary nodules

Using gray level co-occurrence matrix texture features extracted from dual time point PET/CT images to diagnose malignant FDG-avid solitary pulmonary nodules

Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.

Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions

Site-specific weed management in Precision Agriculture is becoming a popular topic among researchers and farmers. The objective of this study was to classify weeds and crop species using RGB image texture features with the comparison of the Support Vector Machine (SVM) classification model and deep learning-based visual group geometry 16 (VGG16) classification models. A total of 3792 RGB images of crop and weed samples were captured from the greenhouse, including 2271 weed images and 1521 crop images. ReliefF feature selection algorithm was applied to select the most important features for prediction models. The SVM and VGG16 deep learning classifiers were used to classify four weeds (horseweed, kochia, ragweed, and waterhemp) and six crop species (black bean, canola, corn, flax, soybean, and sugar beets). Accuracy, f1-score, and kappa score metrics were used to evaluate model performance and data reliability. The VGG16 model classifiers had outperformed all the SVM model classifiers. The results showed that average f1-scores of the VGG16 model classifier were obtained between 93% and 97.5%. The f1-score value of 100% was obtained for the corn class in the VGG16 Weeds-Corn classifier, which seems outstanding for the corn crop production system. This study shows promising results of using a deep learning algorithm (VGG16) for weed identification in site-specific weed management in precision agriculture. • Machine learning (ML) and deep learning (DL) based classifiers were built for six types of crop production systems. • Local binary pattern and Gray Level Co-Occurrence Matrix texture features were extracted for ML. • ReliefF feature selection algorithm was used for machine learning feature engineering. • VGG16 classifiers outperformed the SVM classifiers with an f1-score value of over the 93%. • The highest f1-score value of 100% was obtained for the corn class in the Weeds-Corn VGG16 classifier.

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).

Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding.

As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.

Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound

PurposeThe ultrasound (US) diagnosis of breast cancer is usually based on a single-region of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic performance. Multiple regions on multimodal US images of breast tumors may all have useful information for diagnosis. This study aimed to propose a multi-region radiomics approach with multimodal US for artificially intelligent diagnosis of malignant and benign breast tumors. Materials and methodsFirstly, radiomics features were extracted from five regions of interest (ROIs) on B-mode US and contrast-enhanced ultrasound (CEUS) images, including intensity statistics, gray-level co-occurrence matrix texture features and binary texture features. The multiple ROIs included the whole tumor region, strongest perfusion region, marginal region and surrounding region. Secondly, a deep neural network, composed of the point-wise gated Boltzmann machine and the restricted Boltzmann machine, was adopted to comprehensively learn and select features. Thirdly, the support vector machine was used for classification between benign and malignant breast tumors. Finally, five single-region classification models were generated from five ROIs, and they were fused to form an integrated classification model. ResultsExperimental evaluation was conducted on multimodal US images of breast from 187 patients with breast tumors (68 malignant and 119 benign). Under five-fold cross-validation, the classification accuracy, sensitivity, specificity, Youden's index and area under the receiver operating characteristic curve (AUC) with our model were 87.1% ± 3.3%, 77.4% ± 11.8%, 92.4% ± 7.2%, 69.8% ± 8.6% and 0.849 ± 0.043, respectively. Our model was significantly better than single-region single-modal methods in terms of the AUC and accuracy (p < 0.05). ConclusionIn addition to the whole tumor region, the other regions including the strongest perfusion region, marginal region and surrounding region on US images can assist breast cancer diagnosis. The multi-region multimodal radiomics model achieved the best classification results. Our artificially intelligent model would be potentially useful for clinical diagnosis of breast cancer.

Texture analysis of the microstructure of internal curing concrete based on image recognition technology

Mechanical properties of internal curing concrete are greatly affected by its physical properties such as water content, cementing material content, porosity, and saturation. At the micro- level, such impact is finally reflected in the surface texture of its materials. In this study, the image recognition technology was used to find that the internal curing concrete samples have significant micromorphology and texture features. A texture parameter–strength model was established based on the relationship between Tamura texture parameters, gray level co-occurrence matrix (GLCM) texture parameters, and the mechanical strength. Due to the sensitivity of material properties and parameters, in terms of Tamura texture characteristics, coarseness, regularity and directionality are effective parameters for predicting the compressive strength of internally curing concrete. In terms of the GLCM texture, energy, correlation, entropy, and contrast are effective parameters to predict the compressive strength of the internal curing concrete. Correlations between each texture parameter and compressive strength follow different laws.

Brain Tumor Segmentation From Multimodal MRI Data Based on GLCM and SVM Classifier

The segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure.This manuscript per the authors focus on the effects of GLCM texture on brain tumor segmentation. The result is different from the application of the GLCM texture in other types of image processing.The experimental material was a dataset called BraTs2015. The segmented five different labels are normal brain, necrosis, edema, non-enhancing tumor, and enhancing tumor. The proposed model was verified with the Dice coefficient. The result demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests

Forest degradation has been most frequently defined as an anthropogenic reduction in biomass compared with reference biomass in extant forests. However, so-defined “degraded forests” may widely vary in terms of recoverability. A prolonged loss of recoverability, commonly described as a loss of resilience, poses a true threat to global environments. In Bornean logged-over forests, dense thickets of ferns and vines have been observed to cause arrested secondary succession, and their area may indicate the extent of slow biomass recovery. Therefore, we aimed to discriminate the fern thickets and vine-laden forests from those logged-over forests without dense ferns and vines, as well as mapping their distributions, with the aid of Landsat-8 satellite imagery and machine learning modeling. During the process, we tested whether the gray-level co-occurrence matrix (GLCM) textures of Landsat data and Sentinel-1 C-band SAR data were helpful for this classification. Our study sites were Deramakot and Tangkulap Forest Reserves—commercial production forests in Sabah, Malaysian Borneo. First, we flew drones and obtained aerial images that were used as ground truth for the supervised classification. Subsequently, a machine-learning model with a gradient-boosting decision tree was iteratively tested in order to derive the best model for the classification of the vegetation. Finally, the best model was extrapolated to the entire forest reserve and used to map three classes of vegetation (fern thickets, vine-laden forests, and logged-over forests without ferns and vines) and two non-vegetation classes (bare soil and open water). The overall classification accuracy of the best model was 86.6%; however, by combining the fern and vine classes into the same category, the accuracy was improved to 91.5%. The GLCM texture variables were especially effective at separating fern/vine vegetation from the non-degraded forest, but the SAR data showed a limited effect. Our final vegetation map showed that 30.7% of the reserves were occupied by ferns or vines, which may lead to arrested succession. Considering that our study site was once certified as a well-managed forest, the area of degraded forests with a high risk of loss of resilience is expected to be much broader in other Bornean production forests.

Adjacent cartilage tissue structure after successful transplantation: a quantitative MRI study using T2 mapping and texture analysis

ObjectivesThe aim of this study was to assess the texture of repair tissue and tissue adjacent to the repair site after matrix-associated chondrocyte transplantation (MACT) of the knee using gray-level co-occurrence matrix (GLCM) texture analysis of T2 quantitative maps.MethodsTwenty patients derived from the MRI sub-study of multicenter, single-arm phase III study underwent examination on a 3 T MR scanner, including a T2 mapping sequence 12 and 24 months after MACT. Changes between the time points in mean T2 values and 20 GLCM features were assessed for repair tissue, adjacent tissue, and reference cartilage. Differences in T2 values and selected GLCM features between the three cartilage sites at two time points were analyzed using linear mixed-effect models.ResultsA significant decrease in T2 values after MACT, between time points, was observed only in repair cartilage (p < 0.001). Models showed significant differences in GLCM features between repair tissue and reference cartilage, namely, autocorrelation (p < 0.001), correlation (p = 0.015), homogeneity (p = 0.002), contrast (p < 0.001), and difference entropy (p = 0.047). The effect of time was significant in a majority of models with regard to GLCM features (except autocorrelation) (p ≤ 0.001). Values in repair and adjacent tissue became similar to reference tissue over time.ConclusionsGLCM is a useful add-on to T2 mapping in the evaluation of knee cartilage after MACT by increasing the sensitivity to changes in cartilage structure. The results suggest that cartilage tissue adjacent to the repair site heals along with the cartilage implant.Key Points• GLCM is a useful add-on to T2mapping in the evaluation of knee cartilage after MACT by increasing the sensitivity to changes in cartilage structure.• Repair and adjacent tissue became similar to reference tissue over time.• The results suggest that cartilage tissue adjacent to the repair site heals along with the cartilage implant.

Estimating pasture aboveground biomass under an integrated crop-livestock system based on spectral and texture measures derived from UAV images

Pasture production under integrated crop-livestock systems (ICLS) is a key element to support grazing management decisions. Therefore, further experimentation is required to produce reliable estimates of pasture productivity. An unmanned aerial vehicle (UAV) is a viable tool to obtain fast and accurate aboveground biomass (AGB) estimates in pastures under ICLS. We tested several datasets of variables composed of original spectral bands (RGB-NIR), vegetation indices, and gray-level cooccurrence matrix (GLCM) textures to estimate pasture AGB using the random forest (RF) algorithm and feature selection methods in a commercial ICLS farm in western São Paulo State, Brazil. Field measures of pasture AGB were carried out in three field campaigns over five months to capture the spatiotemporal variability of the pasture fields. Most tested models reached similar results on pasture AGB estimates (R2: 0.60 to 0.70), while the number of variables selected for the RF algorithm differed among models (from 6 to 160 variables). The three more accurate models used few variables, two of which used only texture measures, while the third also employed combined spectral bands and vegetation indices. The best model (R2 = 0.70) used only 10 texture measures. Texture measures that represented images’ inner patterns, calculated from NIR, red-edge, and triangular greenness index data, were the most relevant variables for the main models. The texture contribution indicates important information to be considered to estimate pasture AGB when using UAV imagery. The results achieved by UAV estimates allow the design of multitemporal AGB pasture maps, which may be useful for building more reliable spatiotemporal data.

Solar illumination effects on the dry-season variability of spectral and spatial attributes calculated from PlanetScope data over tropical forests of the Amazon

ABSTRACT The spectral variability of tropical forests during the Amazonian dry season is not entirely understood because of the divergent responses in Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) measured under-increased water deficit and high insolation. Here, we used a dataset composed of 493 cloud-free PlanetScope (PS) images to investigate possible effects of solar illumination on the dry-season variability of spectral and spatial attributes. The attributes were calculated from June to September over dense tropical forests of the Amazon. The dry-season images were obtained at nadir viewing between 2017 and 2019 over 12 selected sites representing different climatic and environmental conditions. To detect dry-season patterns of vegetation brightness with changes in the geometry of image acquisition, we applied principal component analysis (PCA) over the PS surface reflectance. We plotted the average surface reflectance (2017–2019) for each of the four PS bands and inspected the variability of two VIs with distinct levels of anisotropy to bidirectional effects: the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We also investigated the signal of textural metrics from Grey Level Co-occurrence Matrix (GLCM) obtained from the near-infrared (NIR) band of PS. Finally, we generated shade fractions from Spectral Mixture Analysis (SMA), correlated the spectral and spatial attributes of vegetation with solar angles, and observed the dry-season variability in reflectance and VIs over pseudo-invariant soil surfaces. The results showed the existence of solar illumination effects on PS image acquisition during the dry season of the Amazon, which affected differently the NDVI and EVI. From the beginning (June) to the end (September) of the dry season, the solar zenith angle (SZA) decreased and the solar azimuth angle (SAA) increased during the period of acquisition of the PS images. The amplitude of SZA between June and September increased towards south of the Amazon, while the amplitude of SAA increased towards north of this region. Changes in vegetation brightness from June to September were captured by PCA over some sites. Because of the overall increase in both red and NIR band reflectance, solar illumination effects were compensated during the NDVI calculation. In contrast, because the EVI is largely driven by changes in NIR reflectance, these effects contributed to increase the EVI signal at the end of the dry season. For most sites, GLCM texture mean increased towards the end of the dry season, while texture variance decreased in the opposite direction. Shade fractions decreased towards September when reduced amounts of canopy shadows were sensed by PS. EVI was more anisotropic than NDVI and presented higher negative correlations with SZA and shade fractions and higher positive correlations with SAA and texture mean. The dry-season increase in EVI with solar illumination effects was also observed over pseudo-invariant soil surfaces. From this unprecedent scale of observations at high spatial and temporal resolutions, we recommend caution when using anisotropic VIs for large-scale phenological studies over the Amazon because biophysical and non-biophysical signals may be coupled together.

Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images.

Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms—bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)—was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.

Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline-Alkali Soil.

Desiccation cracking during water evaporation is a common phenomenon in soda saline–alkali soils and is mainly determined by soil salinity. Therefore, quantitative measurement of the surface cracking status of soda saline–alkali soils is highly significant in different applications. Texture features can help to determine the mechanical properties of soda saline–alkali soils, thus improving the understanding of the mechanism of desiccation cracking in saline–alkali soils. This study aims to provide a new standard describing the surface cracking conditions of soda saline–alkali soil on the basis of gray-level co-occurrence matrix (GLCM) texture analysis and to quantitatively study the responses of GLCM texture features to soil salinity. To achieve this, images of 200 field soil samples with different surface cracks were processed and calculated for GLCMs under different parameters, including directions, gray levels, and step sizes. Subsequently, correlation analysis was then conducted between texture features and electrical conductivity (EC) values. The results indicated that direction had little effect on the GLCM texture features, and that four selected texture features, contrast (CON), angular second moment (ASM), entropy (ENT), and homogeneity (HOM), were the most correlated with EC under a gray level of 2 and step size of 1 pixel. The results also showed that logarithmic models can be used to accurately describe the relationships between EC values and GLCM texture features of soda saline–alkali soils in the Songnen Plain of China, with calibration R2 ranging from 0.88 to 0.92, and RMSE from 2.12 × 10−4 to 9.68 × 10−3, respectively. This study can therefore enhance the understanding of desiccation cracking of salt-affected soil to a certain extent and can also help to improve the detection accuracy of soil salinity.

A shallow extraction of texture features for classification of abnormal video endoscopy frames

Automated analysis of the gastric lesions in endoscopy videos is a challenging task and dynamics of the gastrointestinal environment make it even more difficult. In computer-aided diagnosis, gastric images are analyzed by visual descriptors. Various Deep Convolutional Neural Network (DCNN) models are available for representation learning and classification. In this paper, a computer aided diagnosis system is presented for the classification of abnormalities in Videos Endoscopy (VE) images based on Deep Gray-Level Co-occurrence Matrix (DeepGLCM) texture features. In our scheme, the convolutional layers of an already trained model are employed for acquisition of the statistical features from responses of filters to estimate the texture representation of VE frames. A learning model is trained on these features for gastric frames classification. The results obtained by using public datasets of endoscopy images to calculate the performance of the proposed method. In addition, we also use a private endoscopy dataset which is acquired from the University of Aveiro. The DeepGLCM outperforms by achieving the average accuracy of ≈92% and 0.96 area under the curve (AUC) for the chromoendoscopy (CH) dataset and ≈85% accuracy for Confocal Laser Endomicroscopy (CLE) and white light video endoscopy datasets. It is evident that the DeepGLCM texture features provide a better representation than the traditional texture extraction methods by efficiently dealing with variance in images due to different imaging technologies.

Semi-automatic segmentation of skin lesions based on superpixels and hybrid texture information.

Dermoscopic images are commonly used in the early diagnosis of skin lesions, and several computational systems have been proposed to analyze them. The segmentation of the lesions is a fundamental step in many of these systems. Therefore, a semi-automatic segmentation method is proposed here, which begins by building the superpixels of the image under analysis based on the zero parameter version of the simple linear iterative clustering (SLIC0) algorithm. Then, each superpixel is represented using a descriptor built by combining the grey-level co-occurrence matrix and Tamura texture features. Afterward, the gain ratios of the features are used to select the input for the semi-supervised seeded fuzzy C-means clustering algorithm. Hence, from a few specialist-selected superpixels, this clustering algorithm groups the built superpixels into lesion or background regions. Finally, the segmented image undergoes a post-processing step to eliminate sharp edges. The experiments were performed on 1380 images: 401 images from the PH2 and DermIS datasets, which were used to establish the parameters of the method, and 3,573 images from the ISIC 2016, ISIC 2017 and ISIC 2018 datasets were used for the analysis of the method's performance. The findings suggest that, by manually identifying just a few of the generated superpixels, the method can achieve an average segmentation accuracy of 96.78%, which confirms its superiority to the ones in the literature.

Comparative study on the abilities of different crack parameters to estimate the salinity of soda saline-alkali soil in Songnen Plain, China

Salinity is commonly considered to be the main factor determining the crack extent of clayey soda saline-alkali soil in the Songnen Plain in China. However, there are great variations among different parameters in quantifying the extent of desiccation cracks in salt-affected soil, leading to substantial uncertainty in their abilities to characterize soil salinity. This study aimed to analyze the relationships between different types of crack parameters and the salinity of soil samples to establish optimal prediction models for the electrical conductivity (EC) values. To achieve these objectives, 104 soil samples with different salinity levels were obtained and pre-processed based on a unified standard for binary crack patterns. Thereafter, three common types of crack parameters including the crack intensity factor (CIF); box-counting dimensions under equal-partitions of 2, 3, 5, and 7; and four gray-level co-occurrence matrix (GLCM) texture features were extracted for correlation coefficients with the EC values of the soil samples based on a series of regression analyses. The results indicate that although different types of crack parameters all have a clear correlation with EC, CIF shows a poor relationship with EC values compared to those of the fractal dimensions and the GLCM texture features. The CIF, box-counting fractal dimensions based on 2 equal-partition, and contrast (CON) texture feature were then selected as the most ideal indices to develop exponential regression models for EC estimation. In addition, fitting results from verification work showed that CON was the best predictor of EC (R2 of 0.85), with an accuracy much higher than box-counting dimension based on 2 equal-partition (R2 of 0.68) and CIF (R2 of only 0.56).

Mapping invasive strawberry guava (Psidium cattleianum) in tropical forests of Mauritius with Sentinel-2 and machine learning

ABSTRACT Invasive flora alien species (IAS) pose a serious threat to biodiversity in tropical forests. This study evaluated different classification approaches for remote sensing of the target IAS, strawberry guava (Psidium cattleianum), using open-access satellite imagery to aid research and understanding of IAS impacts in tropical forests. This is likely the first study using freely available open-access imagery to map strawberry guava, and the first mapping of invasive species in Mauritius using remote sensing. Ground-based observations in Black River Gorges National Park (BRGNP), Mauritius, resulted in 4670 ‘ground truth’ samples across three land cover classes representing different levels of strawberry guava canopy cover; 70% of these ground truth data were used for training and model tuning, and 30% were held-out for testing. Classification was performed using Sentinel-2 MSI images and Google Earth Engine. Computation of Jeffries-Matusita distance supported the addition of Grey Level Co-Occurrence Matrix texture measures, and spectral indices (ARI1, and ReNDVI) for classification, since these features increased separability of strawberry guava cover classes substantially over using spectral bands alone. Higher than 80% overall and individual class accuracies were achieved with both Random Forest (RF) and Support Vector Machine (SVM) classification of strawberry guava in BRGNP. RF is recommended for similar future applications for its higher overall accuracy (97.60% ± .20% at 95% confidence), lower variation, and prediction of more homogenous class regions. This study found that strawberry guava canopy cover in BRGNP was highest in central regions and correlated to steeper slopes, with little overall change from 2016 to 2020.

Potential of ALOS-2 PALSAR-2 StripMap data for lithofacies identification and geological lineament mapping in vegetated fold-thrust belt of Nagaland, India

• Geological mapping of vegetation-covered NFTB is done with ALOS-2 SAR data. • Second order lithological variance could be discriminated by this process. • Meso-scaled geological lineaments has been mapped and analysed. In this study statics-based textural analysis GLCM (Grey Level Co-occurrence Matrix) of high resolution L band SAR data from ALOS-2 of JAXA has been used to discriminate between lithological facies of the fold-thrust belt of Nagaland, India. Sandstones, shales, siltstones and clays have been separated in the analysis. Importantly, the lithological facies with second-order compositional variance have been distinguished from each other. The study of 27 lithological pairs indicates a Transformed Divergence index greater than 1.7 except one pair which shows an index of 1.6. The average GLCM texture values of different stratigraphic units in VV and VH polarization shows appreciable differences. Coefficient of variation of the texture samples from each descriptor show uniform distribution around their mean. The mean has thus been used as a lithological index in this study. Further, the medium scaled geological lineaments have been extracted and mapped at 1: 25,000 scale. The Compass Edge Detector on the first Principal Component of full polarized ALOS-2 scene has been used for this purpose. Fracture network, outcrop length, density, and orientation have also been analyzed. The study is successful in rock characterization within vegetation-covered landscape, using L band SAR remote sensing. It may provide an important tool in geological mapping in inaccessible terrains with limited ground data control. The fracture map and its study can provide some useful insights about reservoir characterization and reduce exploration risk in this terrain that has a proven petroleum system.