Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India

Kumar, Chandan; Chatterjee, Snehamoy; Oommen, Thomas; Guha, Arindam

doi:10.1016/j.jag.2019.102006

Cited by 68 publications

(39 citation statements)

References 76 publications

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“…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. In this research, we used MLA over the traditional classification approaches based on spectral distance measurement (Mahalanobis Distance, Minimum Distance, …), due to the fact that the latter are more restrictive and require assumptions about data distribution, which makes lithological mapping complicated in the study area, because each rock unit comprises a linear mixture on a microscopic scale of different minerals with a distinct spectral signature, which justifies their pixels' heterogeneity [51], [52].…”

Section: Introductionmentioning

confidence: 99%

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%

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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“…Mineral spectral features of silicate minerals mostly attributed to vibration of Si-O bonds in TIR region. 14,16,19 Kumar et al 85 proposed an automated lithological mapping approach on AVIRIS-NG hyperspectral data from gold-bearing granite-greenstone belt of the Hutti area (India). In that approach, they employed spectral enhancement techniques such as PCA and ICA and different machine learning algorithms (MLAs) to get an accurate lithologic map (Fig.…”

Section: Lithological Mappingmentioning

confidence: 99%

“…4 (a) Reference lithology map derived from spectral enhancement products using ASTER and (b) lithological classification map generated from SVM using JMIM-based optimum bands of AVIRIS-NG hyperspectral data from gold-bearing granite-greenstone belt of the Hutti area (India). 85 intrusion-related mineral deposits and associated alteration patterns have been shown in Fig. 5.…”

Section: Mineral Mapping and Mineral Explorationmentioning

confidence: 99%

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

Peyghambari

Zhang

2021

J. Appl. Rem. Sens.

127

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Hyperspectral imaging has been used in a variety of geological applications since its advent in the 1970s. In the last few decades, different techniques have been developed by geologists to analyze hyperspectral data in order to quantitatively extract geological information from the high-spectral-resolution remote sensing images. We attempt to review and update various steps of the techniques used in geological information extraction, such as lithological and mineralogical mapping, ore exploration, and environmental geology. The steps include atmospheric correction, dimensionality processing, endmember extraction, and image classification. It is identified that per-pixel and subpixel image classifiers can generate accurate alteration mineral maps. Producing geological maps of different surface materials including minerals and rocks is one of the most important geological applications. The hyperspectral images classification methods demonstrate the potential for being used as a main tool in the mining industry and environmental geology. To exemplify the potential, we also include a few case studies of different geological applications.

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“…It has practical application value to determine rock distribution and avoid cutting high hardness rock with pick. At present, rocks are mainly divided into three categories [14][15][16], namely, igneous rock, sedimentary rock, and metamorphic rock. e types and characteristics of rocks studied in this paper are shown in Table 2.…”

Section: Rock Types and Characteristics Analysismentioning

confidence: 99%

Research on Coal and Rock Type Recognition Based on Mechanical Vision

Zhang

Liu

2021

Shock and Vibration

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In order to identify different kinds of coal, rock, and gangue, the FPV integrated image transmission camera is used to collect images of 6 types of coal, 8 types of rocks, and 2 types of coal gangue, and the images are processed based on the two-dimensional discrete wavelet transform (2D-DWT) based on the steerable pyramid decomposition (SPD). The maximum likelihood estimation method is used to estimate the parameters, and, the coal and rock types are judged by comparing the similarity of each image. The results show the following: (1) in the eight kinds of rocks, the recognition accuracy of shale and limestone is 90%, that of anorthosite is 95%, and those of other rocks are 100%; (2) the accuracy of comprehensive identification of coal, rock, and gangue is 93%, the comprehensive of coal and gangue is 78%, and the rock classification is 97%; (3) the identification time of 6 types of coal samples, 8 types of rock samples, and 2 types of coal gangue samples are in the range of 2 s∼3 s, which is far less than 10 s, which can meet the requirements of coal and rock identification in terms of recognition speed; and (4) according to 20 groups of data, the range, variance, and standard deviation of the same coal gangue sample meet the accuracy requirements of coal and rock identification. The identification method provides an effective method to improve the efficiency of coal separation, effectively determine the distribution of coal and rock, and timely adjust the cutting height of shearer drum and the operation parameters of various fully mechanized mining equipment, so as to improve the recovery rate of coal resources.

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Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India

Cited by 68 publications

References 76 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

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

Research on Coal and Rock Type Recognition Based on Mechanical Vision

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