The Contribution of Sentinel-2 Satellite Images for Geological Mapping in the South of Tafilalet Basin (Eastern Anti-Atlas, Morocco)

Fal, S.; Maanan, Mehdi; Baïdder, Lahssen; Rhinane, Hassan

doi:10.5194/isprs-archives-xlii-4-w12-75-2019

Cited by 24 publications

(6 citation statements)

References 36 publications

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“…The SAM classification technique received the trained ROIs as input. To calculate the degree of similarity between an unknown spectra t and a reference spectra r, the SAM approach has been applied using Equation 4 [26].…”

Section: ) Spectral Angle Mappermentioning

confidence: 99%

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

Khan,

H. Patil,

D. Vibhute

et al. 2024

IJCDS

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Crop classification plays a vital role in crop status monitoring, crop area estimation, and food production. Remote sensing data is widely accepted for crop classification at remote locations. However, crop classification is challenging due to spectral and spatial similarities, complex land structures, temporal inconsistencies, and environmental parameters. Machine learning models must be robust, particularly when dealing with a variety of crop types and changing environmental factors. This study examines the extent to which various algorithms generalize, emphasizing the importance of adaptability to various farming systems and geographical conditions. It explores transfer learning and ensemble approaches as possible ways to improve the resilience and flexibility of the model. In the present study, an effort has been made to identify, classify, and map multiple crops from the complex environment using the Sentinel-2 dataset and advanced machine learning methods such as random forest, Spectral Angle Mapper (SAM), Maximum Likelihood Classifier (MLC), K-means clustering and Iterative Self-Organizing Data Analysis Technique (ISODATA). The crop spectral features were identified using the Normalized Difference Vegetation Index (NDVI). The NDVI outcomes ranged between -0.91 and 0.54, which were then used to identify crop areas. Ground reference data, Google map, and Google Earth data were used to determine the crop classes, train the data, and validate the results. The five major crops viz. Cotton, Paddy, Orchard, Yellow split Pigeon peas, Chickpeas, and Other crops were identified and classified efficiently. According to the experimental results, the random forest approach had the best overall accuracy, 87.71%, and a kappa value of 0.86 than other methods. Alternatively, the ISODATA method provided an overall accuracy of 85.01% with a kappa value of 0.82. The agricultural decision-makers can use the results of this study for decision-making and management.

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Section: ) Spectral Angle Mappermentioning

confidence: 99%

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

Khan,

H. Patil,

D. Vibhute

et al. 2024

IJCDS

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“…The first articles in the literature regarding this satellite address their potential to alteration and iron/gossan mapping, while others highlight the correspondence between ASTER, Landsat 8, and Sentinel-2 VNIR and SWIR bands [90][91][92][93]. More recent studies highlight the possible contribution and suitability of Sentinel-2 images for geological mapping [94,95] and for geological fault detection [96]. Ge et al [97] applied different MLAs, Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN), RF and SVM, to Sentinel-2 images in order to perform lithological classification in an ophiolite complex in Inner Mongolia, China.…”

Section: Data Products Used For the Researchmentioning

confidence: 99%

Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives

et al. 2020

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Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration.

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“…The RF algorithm has also been considered as the best model with reported accuracies of 91% [40] after application to Sentinel-2 and 85.75% [41] with application to a fusion of Sentinel-2+ASTER+DEM data. In other case studies, the maximum likelihood classifier (MLC) has been reported as the best, with accuracies of 70% [42] and 76% [43] applied to Sentinel-2 data. Therefore, different ML algorithms perform better in different lithological formations for a diverse choice of satellite data.…”

Section: Introductionmentioning

confidence: 97%

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

et al. 2022

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Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

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The Contribution of Sentinel-2 Satellite Images for Geological Mapping in the South of Tafilalet Basin (Eastern Anti-Atlas, Morocco)

Cited by 24 publications

References 36 publications

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

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