A comparison of GEOBIA Vs PBIA machine learning methods for lithological mapping using Sentinel 2 imagery: Case study of Skhour Rehamna, Morocco

Serbouti, Imane; Raji, Mohammed; Hakdaoui, Mustapha

doi:10.1109/morgeo49228.2020.9121899

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…the textural and spectral properties [27], [28], thus, image objects accordingly contain supplemental spectral and spatial attributes compared to individual pixels [29]. On the basis of the resultant segmented images, numerous MLAs are applied in GEOBIA approach such as k-NN classifier [30] to extract thematic maps, nevertheless, the supervised classifier support vector machine (SVM) is one of the most effective algorithm in GEOBIA approach for land use land cover mapping (LULC) [31], it has also has been used in this study to evaluate its performance in the field of geological mapping. Consequently, object-based approach has been widely used for several studies, including LULC classification [32], [33], change detection [34], [35], urban mapping [36], landform mapping [37]- [39], and lithological mapping [30], [40].…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of the resultant segmented images, numerous MLAs are applied in GEOBIA approach such as k-NN classifier [30] to extract thematic maps, nevertheless, the supervised classifier support vector machine (SVM) is one of the most effective algorithm in GEOBIA approach for land use land cover mapping (LULC) [31], it has also has been used in this study to evaluate its performance in the field of geological mapping. Consequently, object-based approach has been widely used for several studies, including LULC classification [32], [33], change detection [34], [35], urban mapping [36], landform mapping [37]- [39], and lithological mapping [30], [40]. Furthermore, most publication that used GEOBIA in geological mapping applied either WorldView-3 (WV-3) along with SVM MLA [41], or the Airborne LiDAR (Li), Airborne Thematic Mapper-9 (ATM9) [42] and Sentinel-2A (S2A) datasets using k-NN algorithm in the classification process [30].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, object-based approach has been widely used for several studies, including LULC classification [32], [33], change detection [34], [35], urban mapping [36], landform mapping [37]- [39], and lithological mapping [30], [40]. Furthermore, most publication that used GEOBIA in geological mapping applied either WorldView-3 (WV-3) along with SVM MLA [41], or the Airborne LiDAR (Li), Airborne Thematic Mapper-9 (ATM9) [42] and Sentinel-2A (S2A) datasets using k-NN algorithm in the classification process [30]. Digital image processing tools such as Principal Component Analysis (PCA) [4], [43], Minimum Noise Fraction (MNF) [4], [44], Independent Component Analysis (ICA) [45], [46], Band Ratio (BR) [43], [47] and intensity Hue Saturation (IHS) [49], [50] were extensively used to improve lithological and mineral discrimination, based on enhancing color and features in order to demarcate different rock types.…”

Section: Introductionmentioning

confidence: 99%

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Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta

et al. 2021

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Mapping lithological units of an area using remote sensing data can be broadly grouped into pixel-based (PBIA), sub-pixel based (SPBIA) and object-based (GEOBIA) image analysis approaches. Since it is not only the datasets adequacy but also the correct classification selection that influences the lithological mapping. This research is intended to analyze and evaluate the efficiency of these three approaches for lithological mapping in semi-arid areas, by using Sentinel-2A data and many algorithms for image enhancement and spectral analysis, in particular two specialized Band Ratio (BR) and the Independent component analysis (ICA), for that reason the Paleozoic Massif of Skhour Rehamna, situated in the western Moroccan Meseta was chosen. In this study, the support vector machine (SVM) that is theoretically more efficient machine learning algorithm (MLA) in geological mapping is used in PBIA and GEOBIA approaches. The evaluation and comparison of the performance of these different methods showed that SVM-GEOBIA approach gives the highest overall classification accuracy (OA≈93%) and kappa coefficient (K) of 0,89, while SPBIA classification showed OA of approximately 89% and kappa coefficient of 0,84, whereas the lithological maps resulted from SVM-PBIA method exhibit salt and pepper noise, with a lower OA of 87% and kappa coefficient of 0,80 comparing them with the other classification approaches. From the results of this comparative study, we can conclude that the SVM-GEOBIA classification approach is the most suitable technique for lithological mapping in semi-arid regions, where outcrops are often inaccessible, which complicates classic cartographic work.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta

et al. 2021

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“…One of the most challenging geological applications using remote-sensing-based satellite data is mapping lithological features, especially for large and geologically complex areas that require high-precision lithological maps [1,2]. Furthermore, the limited availability of the spatial and spectral quality of open access image data (e.g., Landsat-5 TM, Landsat-7 ETM+ (enhanced thematic mapper plus), Landsat-8 OLI (operational land imager), ASTER and Sentinel-2) is widely utilized to extract lithological [3][4][5], mineral [6,7] and structural information [8][9][10]. The majority of studies investigating the potential of remote sensing for geological mapping [4,6,[11][12][13] have been conducted over relatively small geographical areas using individual machine learning algorithms (MLAs).…”

Section: Introductionmentioning

confidence: 99%

“…The application of artificial intelligence algorithms has been widely used in the field of geological applications [4,5,13]. Although many studies have attempted to compare dif-ferent algorithms to determine the best classification approach for geological mapping, no consensus on the validity of the available MLAs has yet been achieved.…”

Section: Introductionmentioning

confidence: 99%

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

et al. 2022

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In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.

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Lithological Mapping for a Semi-arid Area Using GEOBIA and PBIA Machine Learning Approaches with Sentinel-2 Imagery: Case Study of Skhour Rehamna, Morocco

Serbouti

Raji

Hakdaoui

2021

Geospatial Intelligence

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A comparison of GEOBIA Vs PBIA machine learning methods for lithological mapping using Sentinel 2 imagery: Case study of Skhour Rehamna, Morocco

Cited by 6 publications

References 30 publications

Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta

Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta

Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

Lithological Mapping for a Semi-arid Area Using GEOBIA and PBIA Machine Learning Approaches with Sentinel-2 Imagery: Case Study of Skhour Rehamna, Morocco

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