Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales

Hall-Beyer, Mryka

doi:10.1080/01431161.2016.1278314

Cited by 273 publications

(220 citation statements)

References 24 publications

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“…Two types of textural measures, namely the grey level co-occurrence matrix (GLCM) and grey level difference vector (GLDV), were generated from each PC1. These texture measures were calculated based on equations as explained in [46]. These measures quantify differences in the grey levels within a local window [47].…”

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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“…Kernel size is one of the most important parameter which defines the context around the reference pixel (Hall-Beyer, 2017). Kernel size takes the number of neighboring pixels need to be considered around center pixel.…”

Section: Generation and Selection Of Glcm Textural Featurementioning

confidence: 99%

Extraction of Built-Up Area by Combining Textural Features and Spectral Indices From Landsat-8 Multispectral Image

Bramhe

Ghosh

Garg

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Remote sensing techniques provide efficient and cost-effective approach to monitor the expansion of built-up area, in comparison to other traditional approaches. For extracting built-up class, one of the common approaches is to use spectral and spatial features such as, Normalized Difference Built-up index (NDBI), GLCM texture, Gabor filters etc. However, it is observed that classes such as river soil and fallow land usually mix up with built-up class due to their close spectral similarity. Intermixing of classes have been observed in the classified image when using spectral channels. In this paper, an approach has been proposed which uses urban based spectral indices and textural features to extract built-up areas. Three well known spectral indices i.e. NDBI, Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBai) have been used in this work. Along with spectral indices, local spatial dependency of neighborhood regions is captured using eight GLCM based textural feature, such as, Contrast, Correlation, Energy and Homogeneity etc. for each image band. All textural and spectral indices bands are combined and used for extracting built-up areas using Support Vector Machine (SVM) classifier. Results suggest 4.91% increase in overall accuracy when using texture and spectral indices in comparison with 84.38% overall accuracy achieved when using spectral data only. It is observed that built-up class are more separable in the projected spectral-spatial feature space in comparison to spectral channels. Incorporation of textural features with spectral features reduces the misclassification error and provides results with less salt and pepper noise.

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“…16), showing an excellent fit of the model to the ground truth data. No information from any other evaluated object feature group, i.e., spectral features based on the mean, mode, SD and textural ones, was selected for the DTs, although texture metrics have often been used for weed detection as this enhances the separation of spectrally similar image regions [49,76]. In that context, [68] combined spectral and textural features to improve the accuracy of a two-class discrimination problem composed of C. dactlylon patches and sugarcane rows.…”

Section: Machine Learning Analysis-features Selectedmentioning

confidence: 99%

Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture

et al. 2019

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The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management. efficient use of farm inputs (e.g., pesticides, fertilizers, water, labor, fuel, etc.) and, consequently, reducing potential environmental impacts caused by the over-application of inputs [1]. Moreover, PV strategies enable farmers to fulfill decision making with regard to the European Union's (EU) Directives included in the Common Agricultural Policy concerning both the digitizing of agriculture and the sustainable use of agricultural inputs, which foster the development of alternative strategies that limit or optimize their usage. PV strategy implementation involves identifying the intra-and inter-crop-field spatial variability and the causes that determine such variability, as well as designing optimal site-specific management strategies accordingly [2]. One of the most innovative technologies that can be employed to quantify this variability is the use of Unmanned Aerial Vehic...

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Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales

Cited by 273 publications

References 24 publications

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Extraction of Built-Up Area by Combining Textural Features and Spectral Indices From Landsat-8 Multispectral Image

Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture

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